├── .idea ├── .gitignore ├── inspectionProfiles │ └── profiles_settings.xml ├── libraries │ └── R_User_Library.xml ├── misc.xml ├── modules.xml ├── vcs.xml └── victor.iml ├── LICENSE ├── README.md ├── docker ├── Dockerfile └── environment.yaml ├── evaluation ├── assembly_evaluation.py ├── average_stats.py ├── contig_abundance_evaluation.py ├── plot_prec_recall.py └── plot_prec_recall_av.py ├── example ├── reads.fa └── ref.fa ├── reproduce.sh └── src ├── bam2clip_fa.py ├── clustering.py ├── compute_ANI.py ├── est_abundance.sh ├── filter_ovlps.py ├── genome_divergence.py ├── reformat_fa.py ├── rm_misassembly.clip.py ├── rm_misassembly.py ├── rm_redundant_genomes.py ├── sort_reads.py ├── strainline.only_iter.sh └── strainline.sh /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /workspace.xml 3 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 7 | -------------------------------------------------------------------------------- /.idea/libraries/R_User_Library.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/victor.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 14 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) <2021> 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Strainline 2 | ## Description 3 | 4 | Haplotype-resolved de novo assembly of highly diverse virus genomes is critical in prevention, control and treatment of viral diseases. Current methods either can handle only relatively accurate short read data, or collapse haplotype-specific variations into consensus sequence. Here, we present Strainline, a novel approach to assemble viral haplotypes from noisy long reads without a reference genome. As a crucial consequence, Strainline is the first approach to provide strain-resolved, full-length de novo assemblies of viral quasispecies from noisy third-generation sequencing data. Benchmarking experiments on both simulated and real datasets of varying complexity and diversity confirm this novelty, by demonstrating the superiority of Strainline in terms of relevant criteria in comparison with the state of the art. 5 | 6 | ## Installation and dependencies 7 | 8 | Strainline relies on the following dependencies: 9 | - [minimap2](https://github.com/lh3/minimap2) 10 | - [daccord](https://github.com/gt1/daccord) 11 | - [samtools](http://www.htslib.org/) 12 | - [spoa](https://github.com/rvaser/spoa) 13 | - `jgi_summarize_bam_contig_depths` program from [metabat2](https://bitbucket.org/berkeleylab/metabat/src/master/) 14 | - Python v3.6+ 15 | 16 | 17 | To run Strainline, firstly, it is recommended to install the dependencies through [Conda](https://docs.conda.io/en/latest/). 18 | Also, [DAZZ_DB](https://github.com/thegenemyers/DAZZ_DB) and [DALIGNER](https://github.com/thegenemyers/DALIGNER) 19 | are required before running `daccord`. 20 | ``` 21 | conda create -n strainline 22 | conda activate strainline 23 | conda install -c bioconda minimap2 spoa samtools dazz_db daligner metabat2 24 | ``` 25 | Then, one could just download executable program and link `daccord` to the `/path/to/envs/strainline/bin/`. Change the `/path/to/` as your own path to conda. Make sure that `daccord -h` can work successfully. 26 | ``` 27 | wget https://github.com/gt1/daccord/releases/download/0.0.10-release-20170526170720/daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu.tar.gz 28 | tar -zvxf daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu.tar.gz 29 | ln -fs $PWD/daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu/bin/daccord /path/to/envs/strainline/bin/daccord 30 | ``` 31 | 32 | You can also install with docker directly, please see the end. 33 | 34 | ## Running and options 35 | The input read file is required and the format should be FASTA. Other parameters are optional. 36 | Please run `strainline.sh -h` to get details of optional parameters setting. 37 | Before running Strainline, please read through the following basic parameter settings, 38 | which may be helpful to achieve better assemblies. 39 | ``` 40 | Usage: strainline.sh [options] -i reads.fasta -o out -p sequencingPlatform 41 | 42 | Input: 43 | reads.fasta: fasta file of input long reads. 44 | out: directory where to output the results. 45 | sequencingPlatform: long read sequencing platform: PacBio (-p pb) or Oxford Nanopore (-p ont) 46 | 47 | Options: 48 | --minTrimmedLen INT: Minimum trimmed read length. (default: 1000) 49 | --topk INT, -k INT: Choose top k seed reads. (default: 50) 50 | --minOvlpLen INT: Minimum read overlap length. (default: 1000) 51 | --minIdentity FLOAT: Minimum identity of overlaps. (default: 0.99) 52 | --minSeedLen INT: Minimum seed read length. (default: 3000) 53 | --maxOH INT: Maximum overhang length allowed for overlaps. (default: 30) 54 | --iter INT: Number of iterations for contig extension. (default: 2) 55 | --maxGD FLOAT: Maximum global divergence allowed for merging haplotypes. (default: 0.01) 56 | --maxLD FLOAT: Maximum local divergence allowed for merging haplotypes. (default: 0.001) 57 | --maxCO INT: Maximum overhang length allowed for contig contains. (default: 5) 58 | --minAbun FLOAT: Minimum abundance for filtering haplotypes (default: 0.02) 59 | --rmMisassembly BOOL: Break contigs at potential misassembled positions (default: False) 60 | --correctErr BOOL: Perform error correction for input reads (default: True) 61 | --threads INT, -t INT: Number of processes to run in parallel (default: 8). 62 | --help, -h: Print this help message. 63 | ``` 64 | 65 | 66 | ## Examples 67 | 68 | One can test the `strainline.sh` program using the small PacBio CLR reads file `example/reads.fa`. Please use the absolute path of `strainline.sh` when running the program. 69 | - PacBio CLR reads 70 | ``` 71 | cd example 72 | /abspath/Strainline/src/strainline.sh -i reads.fa -o out -p pb -k 20 -t 32 73 | ``` 74 | 75 | - ONT reads 76 | ``` 77 | /abspath/Strainline/src/strainline.sh -i reads.fa -o out -p ont -t 32 78 | ``` 79 | 80 | ## Installation with docker and example test 81 | 82 | ``` 83 | git clone https://github.com/HaploKit/Strainline.git 84 | cd Strainline/docker 85 | docker build -t strainline . 86 | 87 | cd ../example 88 | 89 | # 1. run directly in your path with data 90 | docker run -v $PWD:$PWD -w $PWD strainline strainline.sh -i reads.fa -o out -p pb -k 20 -t 16 91 | # 2. start an interactive docker container session and run in your path with data 92 | docker run -it --rm -v $PWD:/wd -w /wd -v /var/run/docker.sock:/var/run/docker.sock strainline /bin/bash 93 | strainline.sh -i reads.fa -o out -p pb -k 20 -t 16 94 | ``` 95 | 96 | ## Citation 97 | Luo, X., Kang, X. & Schönhuth, A. Strainline: full-length de novo viral haplotype reconstruction from noisy long reads. Genome Biol 23, 29 (2022). https://doi.org/10.1186/s13059-021-02587-6 98 | -------------------------------------------------------------------------------- /docker/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM continuumio/miniconda3 2 | SHELL ["/bin/bash", "-c"] 3 | 4 | RUN apt-get update && \ 5 | apt-get install -y build-essential 6 | 7 | RUN mkdir /tools 8 | WORKDIR /tools 9 | COPY environment.yaml . 10 | RUN . /opt/conda/bin/activate && \ 11 | conda env create -n strainline -f environment.yaml && \ 12 | conda clean -a && \ 13 | wget https://github.com/gt1/daccord/releases/download/0.0.10-release-20170526170720/daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu.tar.gz && \ 14 | tar -zvxf daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu.tar.gz && \ 15 | ln -fs /tools/daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu/bin/daccord /opt/conda/envs/strainline/bin/daccord && \ 16 | git clone https://github.com/HaploKit/Strainline.git && \ 17 | rm daccord-0.0.10-release-20170526170720-x86_64-etch-linux-gnu.tar.gz environment.yaml 18 | ENV PATH=/opt/conda/envs/strainline/bin/:$PATH 19 | ENV PATH=/tools/Strainline/src/:$PATH 20 | -------------------------------------------------------------------------------- /docker/environment.yaml: -------------------------------------------------------------------------------- 1 | name: strainline 2 | channels: 3 | - bioconda 4 | - conda-forge 5 | - defaults 6 | dependencies: 7 | - _libgcc_mutex=0.1=conda_forge 8 | - _openmp_mutex=4.5=2_gnu 9 | - bzip2=1.0.8=h7f98852_4 10 | - c-ares=1.20.1=hd590300_1 11 | - ca-certificates=2023.7.22=hbcca054_0 12 | - daligner=1.0.20230620=h031d066_0 13 | - dazz_db=1.0=0 14 | - htslib=1.18=h81da01d_0 15 | - k8=0.2.5=hdcf5f25_4 16 | - keyutils=1.6.1=h166bdaf_0 17 | - krb5=1.21.2=h659d440_0 18 | - ld_impl_linux-64=2.40=h41732ed_0 19 | - libcurl=8.4.0=hca28451_0 20 | - libdeflate=1.19=hd590300_0 21 | - libedit=3.1.20191231=he28a2e2_2 22 | - libev=4.33=h516909a_1 23 | - libffi=3.4.2=h7f98852_5 24 | - libgcc=7.2.0=h69d50b8_2 25 | - libgcc-ng=13.2.0=h807b86a_2 26 | - libgomp=13.2.0=h807b86a_2 27 | - libnghttp2=1.55.1=h47da74e_0 28 | - libnsl=2.0.1=hd590300_0 29 | - libsqlite=3.43.2=h2797004_0 30 | - libssh2=1.11.0=h0841786_0 31 | - libstdcxx-ng=13.2.0=h7e041cc_2 32 | - libuuid=2.38.1=h0b41bf4_0 33 | - libzlib=1.2.13=hd590300_5 34 | - metabat2=2.15=h986a166_1 35 | - minimap2=2.26=he4a0461_2 36 | - ncurses=6.4=h59595ed_2 37 | - openssl=3.1.4=hd590300_0 38 | - perl=5.32.1=4_hd590300_perl5 39 | - pip=23.3.1=pyhd8ed1ab_0 40 | - python=3.10.13=hd12c33a_0_cpython 41 | - readline=8.2=h8228510_1 42 | - samtools=1.18=h50ea8bc_1 43 | - setuptools=68.2.2=pyhd8ed1ab_0 44 | - spoa=4.1.3=hdcf5f25_0 45 | - tk=8.6.13=h2797004_0 46 | - tzdata=2023c=h71feb2d_0 47 | - wheel=0.41.3=pyhd8ed1ab_0 48 | - xz=5.2.6=h166bdaf_0 49 | - zlib=1.2.13=hd590300_5 50 | - zstd=1.5.5=hfc55251_0 51 | prefix: /home/wenhai/miniconda3/envs/strainline 52 | -------------------------------------------------------------------------------- /evaluation/assembly_evaluation.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | import sys 4 | import subprocess 5 | import copy 6 | import multiprocessing as mp 7 | import numpy as np # array, arange, minimum, add (levenshtein) 8 | import itertools # groupby() 9 | import argparse 10 | 11 | def main(): 12 | parser = argparse.ArgumentParser(prog='assembly_evaluation.py', description='Compare assembled contigs to ground truth haplotypes.') 13 | parser.add_argument('contigs', nargs='*', type=str) 14 | parser.add_argument('-gt', '--ground_truth', dest='truth', type=str, required=True) 15 | parser.add_argument('-m', '--max_edit', dest='max_edit', type=float, default=0.05) 16 | parser.add_argument('-c', '--min_HC', dest='min_HC', type=float, default=0.5) 17 | parser.add_argument('-o', '--outdir', dest='outdir', default=".", help="write output files to this directory") 18 | parser.add_argument('-f', '--freq_truth', dest='freq_truth', help="file containing true frequency per ground truth strain") 19 | args = parser.parse_args() 20 | 21 | if not args.contigs: 22 | print("No contig files given. Aborting evaluation.") 23 | sys.exit(1) 24 | 25 | main_outfile = args.outdir.rstrip('/') + '/extra_stats.tsv' 26 | dist_outfile = args.outdir.rstrip('/') + '/min_dist.tsv' 27 | freq_outfile = args.outdir.rstrip('/') + '/freq_true_est.tsv' 28 | assignment_outfile = args.outdir.rstrip('/') + '/contig_assignments.tsv' 29 | 30 | if os.path.exists(freq_outfile): 31 | os.remove(freq_outfile) 32 | if os.path.exists(assignment_outfile): 33 | os.remove(assignment_outfile) 34 | 35 | truth_file = args.truth # "/home/jasmijn/HLA_real/ground_truth/NA12878.HLA-A.fasta" 36 | contig_files = args.contigs # "/home/jasmijn/HLA_real/GIAB_NA12878/savage-lc/contigs_diploid.fasta" 37 | bwa_mode = True 38 | 39 | # read in reference sequences 40 | ground_truth, empty = read_fasta(truth_file) 41 | 42 | # process assemblies 43 | header_line = "Assembly" 44 | sens_line = "Sensitivity" 45 | ppv_line = "PPV" 46 | freq_line = "Rel freq error (%)" 47 | freq_line2 = "Abs freq error (%)" 48 | min_dist_data = {} 49 | for file in contig_files: 50 | header_line += "\t{}".format(file) 51 | stats = evaluate_assembly(file, ground_truth, bwa_mode, args.max_edit, 52 | args.freq_truth, truth_file, args.min_HC, freq_outfile, 53 | args.outdir, assignment_outfile) 54 | sens_line += "\t{:.2f}".format(stats[0]) 55 | ppv_line += "\t{:.2f}".format(stats[1]) 56 | freq = stats[2] 57 | freq_abs = stats[3] 58 | if freq >= 0: 59 | freq_line += "\t{:.2f}".format(freq) 60 | freq_line2 += "\t{:.2f}".format(freq_abs) 61 | else: 62 | freq_line += "\t-" 63 | freq_line2 += "\t-" 64 | min_dist_data[file] = stats[4] 65 | 66 | with open(main_outfile, 'w') as f: 67 | f.write(header_line + '\n') 68 | f.write(sens_line + '\n') 69 | f.write(ppv_line + '\n') 70 | f.write(freq_line + '\n') 71 | f.write(freq_line2 + '\n') 72 | 73 | with open(dist_outfile, 'w') as f: 74 | f.write(header_line + '\n') 75 | for strain in min_dist_data[contig_files[0]].keys(): 76 | line = strain 77 | for file in contig_files: 78 | dist = min_dist_data[file][strain] 79 | if dist == 100: 80 | line += "\t-" 81 | else: 82 | line += "\t{:.1f}".format(min_dist_data[file][strain]) 83 | f.write(line + '\n') 84 | 85 | return 86 | 87 | ################################################################################ 88 | 89 | def evaluate_assembly(contig_file, ground_truth, bwa_mode, max_edit, freq_truth, 90 | truth_file, min_HC, freq_out, outdir, assignment_out): 91 | contigs, ab_est = read_fasta(contig_file, True) 92 | print("------------------------------------------") 93 | print("Number of contigs: {}".format(len(contigs))) 94 | 95 | contigs2aln= {} 96 | 97 | if bwa_mode: 98 | # run bwa mem -a to align contigs to ground truth 99 | print("\nAligning contigs against ground truth...") 100 | sam_file = "{}/contigs_to_truth.sam".format(outdir.rstrip('/')) 101 | subprocess.check_call("bwa index {} 2>/dev/null".format(truth_file), 102 | shell=True) 103 | subprocess.check_call( 104 | "bwa mem -a -L1000 -t 12 {0} {1} > {2} 2>/dev/null".format( 105 | truth_file, contig_file, sam_file), 106 | shell=True) 107 | print() 108 | # score all alignments per contig and select the good alignment(s) 109 | [alignments, unmapped_ids] = read_sam(sam_file) 110 | print("#alignments: {}".format(len(alignments))) 111 | print("#unmapped: {}".format(len(unmapped_ids))) 112 | for contig in contigs: 113 | contigs2aln[contig] = [] 114 | # score and store alignments 115 | for aln in alignments: 116 | #print aln 117 | [contig_id, ref_id] = aln[0:2] 118 | contig_seq = contigs[contig_id] 119 | truth_seq = ground_truth[ref_id] 120 | [score, aln_range] = score_alignment(aln, contig_seq, truth_seq) 121 | if score[0] <= max_edit: 122 | contigs2aln[contig_id].append([score, aln, aln_range]) 123 | os.remove(sam_file) 124 | else: 125 | print("\nPerforming pairwise alignments...") 126 | for contig_id, contig_seq in contigs.items(): 127 | aln_list = [] 128 | for truth_id, truth_seq in ground_truth.items(): 129 | for ori in ["+", "-"]: 130 | if ori == "+": 131 | dt, bt = align2(truth_seq, contig_seq) 132 | else: 133 | dt, bt = align2(truth_seq, revcomp(contig_seq)) 134 | cigar, pos = backtrace_to_cigar(bt) 135 | aln = [contig_id, truth_id, pos, ori, cigar] 136 | [score, aln_range] = score_alignment(aln, contig_seq, truth_seq) 137 | if score[0] <= max_edit: 138 | aln_list.append([score, aln, aln_range]) 139 | contigs2aln[contig_id] = aln_list 140 | 141 | # select optimal alignment(s) 142 | contig_assignments = {} 143 | for contig, aln_list in contigs2aln.items(): 144 | min_score = 1 145 | opt_aln = [] 146 | #print contig 147 | #print aln_list 148 | for i in range(len(aln_list)): 149 | info1 = aln_list[i] 150 | if info1 in opt_aln: 151 | # alignment already paired with another alignment 152 | continue 153 | scores1 = info1[0] 154 | combined = False 155 | for j in range(i+1, len(aln_list)): 156 | info2 = aln_list[j] 157 | # check if these alignments can be combined 158 | if info2[1][1] != info1[1][1]: # not to same ref sequence 159 | continue 160 | elif not (info2[2][1] <= info1[2][0] 161 | or info2[2][0] >= info1[2][1]): 162 | # overlapping alignments 163 | if info2[2][1] >= info1[2][0] and info1[2][1] >= info2[2][0]: 164 | ov_len = info1[2][1]-info2[2][0] 165 | elif info1[2][1] >= info2[2][0] and info2[2][1] >= info1[2][0]: 166 | ov_len = info2[2][1]-info1[2][0] 167 | else: 168 | print("no overlap???") 169 | sys.exit(1) 170 | print("\noverlap of length {} for contig {} on ref {}\n".format(ov_len, contig, info1[1][1])) 171 | ref_len = info1[2][2] 172 | if ov_len > 0.1*ref_len: 173 | print("overlap too long, skipping") 174 | continue 175 | # compute combined score (assigning contig to 2 allele sequences) 176 | combined = True 177 | combined_score = (info1[0][0]*info1[2][2] 178 | + info2[0][0]*info2[2][2]) / (info1[2][2] + info2[2][2]) 179 | # now check if this alignment is optimal 180 | if combined_score == min_score: 181 | ref_id = info1[1][1] 182 | new_aln = True 183 | for aln in opt_aln: 184 | if aln[1][1] != ref_id: 185 | continue 186 | else: 187 | new_aln = False 188 | break 189 | if new_aln: 190 | opt_aln.append(info1) 191 | opt_aln.append(info2) 192 | elif combined_score < min_score: 193 | opt_aln = [info1, info2] 194 | min_score = combined_score 195 | # if there is no pairing, check single alignemnt 196 | if not combined: 197 | scores = scores1 198 | if scores[0] == min_score: 199 | ref_id = info1[1][1] 200 | new_aln = True 201 | for aln in opt_aln: 202 | if aln[1][1] != ref_id: 203 | continue 204 | else: 205 | new_aln = False 206 | break 207 | if new_aln: 208 | opt_aln.append(info1) 209 | elif scores[0] < min_score: 210 | opt_aln = [info1] 211 | min_score = scores[0] 212 | contig_assignments[contig] = opt_aln 213 | count = len(opt_aln) 214 | if count > 1: 215 | print("NOTE: contig {} has {} assignments!".format(contig, count)) 216 | 217 | # build a dict mapping ground truth fragments to contig alignments 218 | truth2aln = {} 219 | for ref_id in ground_truth: 220 | truth2aln[ref_id] = [] 221 | for contig, aln_list in contig_assignments.items(): 222 | for info in aln_list: 223 | scores = info[0] 224 | aln = info[1] 225 | ref_id = aln[1] 226 | aln_range = info[2] 227 | truth2aln[ref_id].append([aln, scores, aln_range]) 228 | 229 | # for ref, aln_list in truth2aln.items(): 230 | # print ref 231 | # for aln in aln_list: 232 | # print aln 233 | 234 | # evaluate assembly 235 | contig_lengths = [len(seq) for contig,seq in contigs.items()] 236 | total_assembly_len = sum(contig_lengths) 237 | N50_all_contigs = compute_NX(contig_lengths, 50) 238 | outfile_line, sensitivity, ppv, min_dist_map = get_assembly_stats(truth2aln, 239 | total_assembly_len, N50_all_contigs, ground_truth, len(contigs2aln), 240 | assignment_out, contig_file) 241 | if freq_truth and len(ab_est) > 0: 242 | av_rel_err, av_abs_err, median = check_frequencies( 243 | freq_truth, ab_est, truth2aln, contigs, ground_truth, min_HC, 244 | freq_out) 245 | if av_rel_err >= 0: 246 | print("average relative abundance error: {:.2f}%".format(av_rel_err)) 247 | print("average absolute abundance error: {:.2f}%".format(av_abs_err)) 248 | print("median abundance error: {:.1f}%\n".format(100*median)) 249 | else: 250 | av_rel_err = -1 251 | av_abs_err = -1 252 | 253 | return sensitivity, ppv, av_rel_err, av_abs_err, min_dist_map 254 | 255 | 256 | def check_frequencies(freq_truth, ab_est, truth2aln, contigs, ground_truth, 257 | min_HC, outfile): 258 | # read true frequencies 259 | true_frequencies = {} 260 | with open(freq_truth, 'r') as f: 261 | for line in f: 262 | [seq_id, freq] = line.strip('\n').split() 263 | true_frequencies[seq_id] = float(freq) 264 | 265 | total_abundance = sum(ab_est.values()) 266 | 267 | # compute relative true frequencies, leaving out any missing haps 268 | total_true_freqs = 0 269 | for truth_id, freq in true_frequencies.items(): 270 | aln_list = truth2aln[truth_id] 271 | truth_len = len(ground_truth[truth_id]) 272 | aln_lengths = [len(contigs[aln[0][0]]) for aln in aln_list] 273 | if len(aln_list) > 0 and max(aln_lengths) > min_HC*truth_len: 274 | total_true_freqs += freq 275 | if total_true_freqs == 0: 276 | print("No contigs of sufficient length, can't evaluate frequencies.\n") 277 | return -1, -1, -1 278 | 279 | total_ab = 0 280 | for truth_id, aln_list in truth2aln.items(): 281 | truth_len = len(ground_truth[truth_id]) 282 | for info in aln_list: 283 | contig_id = info[0][0] 284 | contig_ab = ab_est[contig_id] 285 | if len(contigs[contig_id]) > min_HC*truth_len: 286 | total_ab += contig_ab 287 | if total_ab == 0: 288 | print("No contigs of sufficient length, can't evaluate frequencies.\n") 289 | return -1, -1, -1 290 | 291 | # check assignments and evaluate 292 | err_list = [] 293 | abs_err_list = [] 294 | contigs_seen = [] 295 | f = open(outfile, 'a') 296 | for truth_id, aln_list in truth2aln.items(): 297 | truth_len = len(ground_truth[truth_id]) 298 | true_freq = true_frequencies[truth_id] 299 | if len(aln_list) == 0: # don't evaluate missing strains as wrong estimation 300 | continue 301 | total_ab_est = 0 302 | for info in aln_list: 303 | aln = info[0] 304 | contig_id = aln[0] 305 | if contig_id in contigs_seen: 306 | print("WARNING: duplicate contig assignment") 307 | continue 308 | else: 309 | contigs_seen.append(contig_id) 310 | contig_len = len(contigs[contig_id]) 311 | contig_ab = ab_est[contig_id] 312 | if contig_len > min_HC*truth_len: 313 | # full length contig -> add estimated abundances 314 | total_ab_est += contig_ab 315 | else: 316 | # if not full length, evaluate frequencies manually 317 | print("WARNING: contig not full length") 318 | continue 319 | # return -1, -1 320 | freq_est = total_ab_est/total_ab*100 321 | cor_true_freq = true_freq/total_true_freqs*100 322 | if total_ab_est > 0: 323 | print("{}\t{}".format(cor_true_freq, freq_est)) 324 | f.write("{}\t{}\n".format(cor_true_freq, freq_est)) 325 | abs_err = abs(freq_est - cor_true_freq) 326 | rel_err = abs(freq_est - cor_true_freq)/cor_true_freq 327 | err_list.append(rel_err) 328 | abs_err_list.append(abs_err) 329 | #print truth_id, true_freq, freq_est, len(aln_list) 330 | f.close() 331 | if len(abs_err_list) == 1: 332 | print("Only 1 strain reconstructed, hence perfect frequency estimation.") 333 | return -1, -1, -1 334 | average_rel_err = sum(err_list)/len(err_list)*100 335 | average_abs_err = sum(abs_err_list)/len(abs_err_list) 336 | print("average rel error: {:.2f}%".format(average_rel_err)) 337 | print("average abs error: {:.2f}%".format(average_abs_err)) 338 | print("\nLatex abs/rel error: {:.2f} & {:.2f} \cr\n\n".format(average_abs_err,average_rel_err)) 339 | print("min error: {:.3f}".format(min(err_list))) 340 | print("max error: {:.3f}".format(max(err_list))) 341 | median = np.median(np.array(sorted(err_list))) 342 | return average_rel_err, average_abs_err, median 343 | 344 | def read_fasta(filename, read_ab=False): 345 | # returns ID to sequence dict 346 | id2seq = {} 347 | ab_est = {} 348 | with open(filename, 'r') as f: 349 | seq_id = "" 350 | seq = "" 351 | for line in f: 352 | if line[0] == '>': 353 | if seq_id != "" and seq != "": 354 | id2seq[seq_id] = seq 355 | if read_ab: 356 | ab_est[seq_id] = ab # estimated abundance 357 | seq_id = line.lstrip('>').rstrip('\n').split()[0] 358 | if read_ab: 359 | try: 360 | ab = float(line.lstrip('>').rstrip('\n').split()[-1].lstrip('frequency=')) 361 | except ValueError: 362 | print("WARNING: could not read abundance estimates from fasta") 363 | read_ab = False 364 | seq = "" 365 | else: 366 | seq += line.rstrip('\n') 367 | # add final entry 368 | if seq_id != "" and seq != "": 369 | id2seq[seq_id] = seq 370 | if read_ab: 371 | ab_est[seq_id] = ab # estimated abundance 372 | return id2seq, ab_est 373 | 374 | def read_sam(filename): 375 | # returns a list of all alignments, where each alignment is presented in the 376 | # following format: [seq_id, ref_id, pos, ori, cigar] 377 | aln_list = [] 378 | unmapped_ids = [] 379 | with open(filename, 'r') as f: 380 | for line in f: 381 | if line[0] == "@": 382 | continue 383 | line = line.rstrip('\n').split() 384 | [seq_id, flag, ref_id, pos] = line[0:4] 385 | cigar = line[5] 386 | bits_flag = power_find(int(flag)) 387 | if 4 in bits_flag: # unmapped contig 388 | unmapped_ids.append(seq_id) 389 | continue 390 | elif 16 in bits_flag: # reversed contig 391 | ori = "-" 392 | else: 393 | ori = "+" 394 | aln_list.append([seq_id, ref_id, int(pos)-1, ori, cigar]) # SAM-format is 1-based; switching to 0-based here 395 | return [aln_list, unmapped_ids] 396 | 397 | def power_find(n): 398 | try: 399 | int(n) 400 | except TypeError: 401 | print("power_find TypeError") 402 | print("n = {}".format(n)) 403 | sys.exit(1) 404 | result = [] 405 | binary = bin(n)[:1:-1] 406 | for x in range(len(binary)): 407 | if int(binary[x]): 408 | result.append(2**x) 409 | return result 410 | 411 | def score_alignment(aln, contig, truth): 412 | [seq_id, ref_id, pos, ori, cigar] = aln 413 | if ori == "-": 414 | contig = revcomp(contig) 415 | # split cigar into numbers and characters all separately 416 | splitcigar = ["".join(x) for _, x in itertools.groupby(cigar, 417 | key=str.isdigit)] 418 | # count mismatches, insertions, deletions and N's 419 | mismatch_count = 0 420 | ins_count = 0 421 | ins_len = 0 422 | del_count = 0 423 | del_len = 0 424 | N_count = 0 425 | length = len(contig) 426 | # keep track of position and cigar index 427 | contig_pos = 0 428 | truth_pos = pos 429 | start_pos = pos 430 | i = 0 431 | while i+1 < len(splitcigar): 432 | aln_len = int(splitcigar[i]) 433 | aln_type = splitcigar[i+1] 434 | if aln_type == "S" or aln_type == "H": 435 | if i == 0: # front end clipped 436 | # if pos > 0: 437 | # del_count += 1 438 | # del_len += pos 439 | clipped_truth = min(aln_len, pos) 440 | length -= aln_len - clipped_truth # correct for overhanging contig length 441 | start_pos -= clipped_truth 442 | contig_pos += aln_len 443 | elif i > 0: # back end clipped 444 | clipped_truth = min(aln_len, len(truth)-truth_pos) 445 | # clipped_truth = len(truth) - truth_pos 446 | # if clipped_truth < 0: 447 | # del_count += 1 448 | # del_len += clipped_truth 449 | length -= aln_len - clipped_truth 450 | contig_pos += aln_len 451 | # truth_pos = len(truth) - clipped_truth 452 | truth_pos += clipped_truth 453 | # compare (partially) aligned sequences 454 | contig_part = contig[contig_pos-clipped_truth : contig_pos] 455 | truth_part = truth[truth_pos-clipped_truth : truth_pos] 456 | [mismatches, Ns] = count_mismatches(contig_part, truth_part) 457 | mismatch_count += mismatches 458 | N_count += Ns 459 | elif aln_type == "I": 460 | if i > 0 and i < len(splitcigar)-2: 461 | ins_count += 1 462 | ins_len += aln_len 463 | N_count += contig[contig_pos:contig_pos+aln_len].count('N') 464 | contig_pos += aln_len 465 | elif aln_type == "D": 466 | del_count += 1 467 | del_len += aln_len 468 | truth_pos += aln_len 469 | elif aln_type == "M": 470 | if contig_pos + aln_len > len(contig): 471 | print("contig {} too short?".format(seq_id)) 472 | print(contig_pos, aln_len, len(contig)) 473 | print(pos, cigar) 474 | if truth_pos + aln_len > len(truth): 475 | print("truth too short?") 476 | print(truth_pos, aln_len, len(truth)) 477 | print(pos, cigar) 478 | contig_part = contig[contig_pos : contig_pos + aln_len] 479 | truth_part = truth[truth_pos : truth_pos + aln_len] 480 | [mismatches, Ns] = count_mismatches(contig_part, truth_part) 481 | mismatch_count += mismatches 482 | N_count += Ns 483 | truth_pos += aln_len 484 | contig_pos += aln_len 485 | else: 486 | print("ERROR: cigar string not recognized.") 487 | sys.exit(1) 488 | i += 2 489 | # compute percent identity 490 | if length > 0: 491 | score = (mismatch_count + ins_len + del_len) / float(length) 492 | else: 493 | score = 0 494 | aln_scores = [score, mismatch_count, ins_count, ins_len, del_count, del_len, 495 | N_count] 496 | assert start_pos >= 0 497 | assert truth_pos <= len(truth) 498 | assert start_pos <= truth_pos 499 | aln_range = [start_pos, truth_pos, len(truth)] 500 | return [aln_scores, aln_range] 501 | 502 | def count_mismatches(seq1, seq2): 503 | assert len(seq1) == len(seq2) 504 | mismatches = 0 505 | Ns = 0 506 | for i in range(len(seq1)): 507 | if seq1[i] == "N": # truth has N 508 | Ns += 1 509 | elif seq2[i] == "N": # contig has N 510 | Ns += 1 511 | elif seq1[i] != seq2[i]: 512 | mismatches += 1 513 | return [mismatches, Ns] 514 | 515 | def revcomp(seq): 516 | complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'} 517 | revcomp = "".join(complement.get(base, base) for base in reversed(seq)) 518 | assert len(seq) == len(revcomp) 519 | return revcomp 520 | 521 | def get_assembly_stats(truth2aln, total_assembly_len, N50_all_contigs, 522 | ground_truth, ncontigs, outfile, method): 523 | # prints all assembly statistics of interest 524 | mismatch_count = 0 525 | ins_count = 0 526 | ins_len = 0 527 | del_count = 0 528 | del_len = 0 529 | N_count = 0 530 | target_cov_len = 0 # target bases covered 531 | total_target_len = 0 532 | breakpoints = 0 533 | true_positives = set() 534 | exactly_matched = 0 535 | aln_contig_lengths = [] 536 | min_dist_map = {} 537 | f = open(outfile, 'a') 538 | for truth_id, aln in truth2aln.items(): 539 | print('\n' + truth_id + ':',) 540 | if len(aln) == 0: 541 | truth_seq_len = len(ground_truth[truth_id]) 542 | breakpoints = 0 543 | else: 544 | truth_seq_len = aln[0][2][2] 545 | breakpoints += len(aln)-1 546 | total_target_len += truth_seq_len 547 | target_cov = [0 for i in range(truth_seq_len)] 548 | local_mismatches = 0 549 | local_ins_len = 0 550 | local_del_len = 0 551 | min_dist = 1 552 | for info in aln: 553 | seq_id = info[0][0] 554 | print("{} ({});".format(seq_id, info[0][4]),) 555 | scores = info[1] 556 | mismatch_count += scores[1] 557 | local_mismatches += scores[1] 558 | ins_count += scores[2] 559 | ins_len += scores[3] 560 | local_ins_len += scores[3] 561 | del_count += scores[4] 562 | del_len += scores[5] 563 | local_del_len += scores[5] 564 | N_count += scores[6] 565 | aln_range = info[2] 566 | aln_contig_lengths.append(aln_range[1] - aln_range[0]) 567 | for i in range(aln_range[0], aln_range[1]): 568 | target_cov[i] = 1 569 | dist = scores[1] + scores[3] + scores[5] 570 | if dist == 0: 571 | true_positives.add(seq_id) 572 | assignment = [ 573 | method, seq_id, truth_id, aln_range[1] - aln_range[0], dist, 574 | scores[1], scores[3], scores[5] 575 | ] 576 | f.write('\t'.join([str(x) for x in assignment]) + '\n') 577 | min_dist = min(min_dist, dist/(aln_range[1] - aln_range[0])) 578 | target_cov_len += sum(target_cov) 579 | min_dist_map[truth_id] = min_dist*100 580 | if min_dist == 0: 581 | exactly_matched += 1 582 | print("\ntarget coverage: {} of {} ({:.1f}%)".format(sum(target_cov), truth_seq_len, 100*sum(target_cov)/truth_seq_len)) 583 | print("# contigs: {}".format(len(aln))) 584 | print("minimal contig distance: {:.2f}%".format(min_dist*100)) 585 | print("mismatches, ins_len, del_len: {} {} {}".format(local_mismatches, local_ins_len, local_del_len)) 586 | f.close() 587 | print() 588 | print("-----------------------") 589 | print("- Assembly statistics -") 590 | print("-----------------------") 591 | print("Total assembly length:\t{} bp".format(total_assembly_len)) 592 | if total_assembly_len == 0: 593 | outfile_line = "{0}\t{0}\t{0}\t{0}\t{0}\t{0}\t{0}".format(0) 594 | sensitivity = 0 595 | ppv = 0 596 | return outfile_line, sensitivity, ppv, min_dist_map 597 | total_aln_len = sum(aln_contig_lengths) 598 | total_unaligned_len = total_assembly_len - total_aln_len 599 | unaligned_perc = float(total_unaligned_len)/total_assembly_len*100.0 600 | print("Total unaligned length:\t{} bp ({:.1f}%)".format(total_unaligned_len, unaligned_perc)) 601 | edit_distance = float(mismatch_count + ins_len + del_len)/total_aln_len*100 if total_aln_len > 0 else 0 602 | print("Overall edit distance:\t{:5.3f}%".format(edit_distance)) 603 | mismatch_rate = float(mismatch_count)/total_aln_len*100 if total_aln_len > 0 else 0 604 | print("Mismatch rate:\t{:5.3f}%".format(mismatch_rate)) 605 | N_rate = float(N_count)/total_aln_len*100 if total_aln_len > 0 else 0 606 | print("'N' rate:\t{:5.3f}%".format(N_rate)) 607 | insertion_rate = float(ins_len)/total_aln_len*100 if total_aln_len > 0 else 0 608 | print("Total insertion count:\t{}".format(ins_count)) 609 | print("Total insertion length:\t{} bp ({:5.3f}%)".format(ins_len, insertion_rate)) 610 | #print "Total insertion length:\t{:5.3f}%".format(insertion_rate) 611 | deletion_rate = float(del_len)/total_aln_len*100 if total_aln_len > 0 else 0 612 | print("Total deletion count:\t{}".format(del_count)) 613 | print("Total deletion length:\t{} bp ({:5.3f}%)".format(del_len, deletion_rate)) 614 | #print "Total deletion length:\t{:5.3f}%".format(deletion_rate) 615 | 616 | total_target_cov = float(target_cov_len)/total_target_len*100 617 | print("Target coverage:\t{:4.1f}%".format(total_target_cov)) 618 | 619 | N50_aln = compute_NX(aln_contig_lengths, 50) 620 | print("N50 aligned sequence:\t{}".format(N50_aln)) 621 | print("N50 all contigs:\t{}".format(N50_all_contigs)) 622 | # TODO: minimum contig size to cover at least 50% of the ground truth 623 | 624 | print("Breakpoint number:\t{}".format(breakpoints)) 625 | # TODO: number of unnecessary breakpoints 626 | # TODO: number of conflict cliques larger than known ploidy 627 | print("# true positives = {}".format(len(true_positives))) 628 | sensitivity = exactly_matched/len(truth2aln) 629 | ppv = len(true_positives)/ncontigs 630 | print("Sensitivity = {:.2f}".format(sensitivity)) 631 | print("PPV = {:.2f}".format(ppv)) 632 | print() 633 | outfile_line = "{}\t{}\t{}\t{}\t{:.3f}\t{}\t{:.5f}".format(N50_all_contigs, 634 | total_aln_len, total_target_len, target_cov_len, total_target_cov/100, 635 | mismatch_count+ins_len+del_len, edit_distance/100) 636 | return outfile_line, sensitivity, ppv, min_dist_map 637 | 638 | def compute_NX(contig_lengths, X): 639 | assert X > 0 640 | assert X <= 100 641 | factor = X/100.0 642 | contig_lengths.sort(reverse=True) 643 | total_len = sum(contig_lengths) 644 | current_len = 0 645 | NX = 0 646 | for l in contig_lengths: 647 | current_len += l 648 | if current_len >= factor*total_len: 649 | NX = l 650 | break 651 | assert NX >= 0 652 | return NX 653 | 654 | 655 | if __name__ == '__main__': 656 | sys.exit(main()) 657 | -------------------------------------------------------------------------------- /evaluation/average_stats.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import csv 3 | import sys, os 4 | import argparse 5 | 6 | # df = pd.read_csv("test_quast_report.tsv", sep='\t') 7 | # n_files = len(df.columns)-1 8 | # print(n_files) 9 | 10 | 11 | int_stats = ["# contigs", "N50", "NGA50"] 12 | f1_stats = ["Genome fraction (%)"] 13 | f2_stats = [] 14 | f3_stats = ["Error rate (%)", "Sensitivity", "PPV", "Rel freq error (%)", 15 | "Abs freq error (%)"] 16 | output_stats = ["# contigs", "Genome fraction (%)", "N50", "NGA50", 17 | "Error rate (%)", "Sensitivity", "PPV", "Rel freq error (%)", 18 | "Abs freq error (%)"] 19 | 20 | ER_stats = [ 21 | "# N's per 100 kbp", 22 | "# mismatches per 100 kbp", 23 | "# indels per 100 kbp" 24 | ] 25 | suppl_stats = [ 26 | "# N's per 100 kbp", 27 | "# mismatches per 100 kbp", 28 | "# indels per 100 kbp", 29 | "Unaligned length" 30 | ] 31 | 32 | def main(): 33 | parser = argparse.ArgumentParser(prog='average_stats.py', description='Compute average assembly statistics from multiple input files.') 34 | parser.add_argument('reports', nargs='*', type=str) 35 | parser.add_argument('--dist', action='store_true') 36 | parser.add_argument('--skip_freq', action='store_true') 37 | parser.add_argument('--suppl', action='store_true') 38 | args = parser.parse_args() 39 | 40 | if not args.reports: 41 | print("No input files given. Use --help for usage information.") 42 | sys.exit(1) 43 | quast_reports = args.reports 44 | # quast_reports = ['test_quast_report.tsv'] 45 | 46 | if args.dist: 47 | stats = [] 48 | with open(quast_reports[0], 'r') as f: 49 | for line in f: 50 | strain = line.rstrip('\n').split('\t')[0] 51 | stats.append(strain) 52 | stats = sorted(stats[1:]) 53 | else: 54 | if args.suppl: 55 | stats = output_stats + suppl_stats 56 | else: 57 | stats = output_stats 58 | if args.skip_freq: 59 | stats.remove("Rel freq error (%)") 60 | stats.remove("Abs freq error (%)") 61 | 62 | latex_table = [] 63 | print('\nfilename\t' + '\t'.join(stats)) 64 | for file in quast_reports: 65 | results = compute_average_stats(file, stats+ER_stats) 66 | result_line = file 67 | latex_line = file 68 | for stat in stats: 69 | if stat == "Error rate (%)": 70 | result = sum([results[stat] for stat in ER_stats])/1000 71 | else: 72 | try: 73 | result = results[stat] 74 | except KeyError: 75 | result = '-' 76 | # format output with desired floating point precision 77 | if result == '-': 78 | result_line += '\t{}'.format(result) 79 | latex_line += ' & {}'.format(result) 80 | elif stat in int_stats + suppl_stats: 81 | result_line += '\t{}'.format(int(round(result))) 82 | latex_line += ' & {}'.format(int(round(result))) 83 | elif stat in f1_stats: 84 | result_line += '\t{:.1f}'.format(result) 85 | latex_line += ' & {:.1f}'.format(result) 86 | elif stat in f2_stats: 87 | result_line += '\t{:.2f}'.format(result) 88 | latex_line += ' & {:.2f}'.format(result) 89 | elif stat in f3_stats: 90 | result_line += '\t{:.3f}'.format(result) 91 | latex_line += ' & {:.3f}'.format(result) 92 | elif args.dist: 93 | result_line += '\t{:.1f}'.format(result) 94 | latex_line += ' & {:.1f}'.format(result) 95 | print(result_line) 96 | latex_table.append(latex_line + '\\\\') 97 | print() 98 | print('\n'.join(latex_table)) 99 | print() 100 | 101 | return 102 | 103 | 104 | def compute_average_stats(file, stats="all"): 105 | results = {} 106 | with open(file, 'r') as tsv: 107 | tsv = csv.reader(tsv, delimiter='\t') 108 | for line in tsv: 109 | # print(line) 110 | stat = line[0] 111 | values = [x for x in line[1:] if x!='-'] 112 | if stats=="all" or stat in stats: 113 | try: 114 | av = sum([float(x) for x in values])/len(values) 115 | results[stat] = av 116 | except ZeroDivisionError as e: 117 | # all values were '-' i.e. not available 118 | av = '-' 119 | # print(stat, av) 120 | return results 121 | 122 | if __name__ == '__main__': 123 | sys.exit(main()) 124 | -------------------------------------------------------------------------------- /evaluation/contig_abundance_evaluation.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | import sys 4 | import subprocess 5 | import copy 6 | import multiprocessing as mp 7 | import numpy as np # array, arange, minimum, add (levenshtein) 8 | import itertools # groupby() 9 | import argparse 10 | 11 | def main(): 12 | parser = argparse.ArgumentParser(prog='assembly_evaluation.py', description='Compare assembled contigs to ground truth haplotypes.') 13 | parser.add_argument('-c', '--contigs', type=str, required=True) 14 | parser.add_argument('-gt', '--ground_truth', dest='truth', type=str, required=True) 15 | parser.add_argument('-m', '--max_edit', dest='max_edit', type=float, default=0) 16 | parser.add_argument('-o', '--outdir', dest='outdir', default=".", help="write output files to this directory") 17 | parser.add_argument('-f', '--freq_truth', dest='freq_truth', help="file containing true frequency per ground truth strain") 18 | parser.add_argument('--total_cov', dest='total_cov', type=float, required=True) 19 | args = parser.parse_args() 20 | 21 | print("-----------------------------------------------") 22 | if not args.contigs: 23 | print("No contig file given. Aborting evaluation.") 24 | sys.exit(1) 25 | else: 26 | print("Contig file = {}".format(args.contigs)) 27 | print("Truth file = {}".format(args.truth)) 28 | 29 | # read contig abundance estimates and true haplotypes 30 | contigs, ab_est = read_fasta(args.contigs, read_ab=True) 31 | ground_truth, empty = read_fasta(args.truth) 32 | freq_truth = read_frequencies(args.freq_truth) 33 | print("Number of contigs: {}".format(len(contigs))) 34 | print("Number of reference sequences: {}".format(len(ground_truth))) 35 | if not contigs: 36 | print("No contigs found, exiting.") 37 | sys.exit(1) 38 | 39 | # assign contigs to strains -- note that: 40 | # (1) a contig may be assigned to multiple strains (conserved region) 41 | # (2) multiple contigs could map to the same strain, in the same region, 42 | # due to uncorrected errors. To avoid this situation we only evaluate 43 | # perfectly matching contigs 44 | # format: {contig id : [score, aln, aln_range]} 45 | contig_assignments = assign_contigs(args.contigs, args.truth, args.max_edit, 46 | args.outdir) 47 | 48 | # how many contigs match perfectly? 49 | evaluation_ratio = len(contig_assignments) / len(contigs) 50 | discarded = len(contigs) - len(contig_assignments) 51 | print("# contigs excluded from evaluation = {}".format(discarded)) 52 | print("Evaluation ratio = {:.2f}\n".format(evaluation_ratio)) 53 | 54 | total_assembly_freq = 0 55 | assembled_seqs = set([x[1][1] for l in contig_assignments.values() for x in l]) 56 | for seq, freq in freq_truth.items(): 57 | if seq in assembled_seqs: 58 | total_assembly_freq += freq 59 | 60 | # calculate true contig abundances 61 | results = [] 62 | with open("{}/contig_abundance_estimates.txt".format(args.outdir), 'w') as f: 63 | for contig_id, assignment in contig_assignments.items(): 64 | true_freq = calculate_freq(assignment, freq_truth, total_assembly_freq) 65 | true_ab = true_freq * args.total_cov 66 | estimate = ab_est[contig_id] 67 | results.append((true_ab, estimate)) 68 | pos = assignment[0][1][2] 69 | f.write("{} {} {}\n".format(true_ab, estimate, pos)) 70 | print(contig_id, assignment[0][1][1], len(contigs[contig_id]), 71 | true_ab, estimate) 72 | if not results: 73 | print("No results calculated, exiting.") 74 | sys.exit(1) 75 | 76 | # evaluate estimates 77 | total_error = 0 78 | abs_err = 0 79 | for (v1, v2) in results: 80 | if v1 + v2 > 0: 81 | total_error += abs(v1-v2) / (0.5 * (v1+v2)) 82 | abs_err += abs(v1-v2) / args.total_cov 83 | rel_error = total_error / len(results) * 100 84 | abs_err = abs_err / len(results) * 100 85 | print("Relative abundance estimation error = {:.1f}%".format(rel_error)) 86 | # print("Absolute abundance estimation error = {:.1f}%".format(abs_err)) 87 | print("-----------------------------------------------") 88 | return 89 | 90 | 91 | def calculate_freq(assignment, freq_truth, total_assembly_freq): 92 | true_freq = 0 93 | for aln_info in assignment: 94 | [score, aln, aln_range] = aln_info 95 | truth_id = aln[1] 96 | true_freq += freq_truth[truth_id] 97 | if true_freq == 0: 98 | print(assignment) 99 | assert true_freq > 0 100 | return true_freq / total_assembly_freq 101 | 102 | 103 | def assign_contigs(contig_file, truth_file, max_edit, outdir): 104 | contigs, empty = read_fasta(contig_file) 105 | ground_truth, empty = read_fasta(truth_file) 106 | contigs2aln= {contig_id : [] for contig_id in contigs.keys()} 107 | 108 | # run bwa mem -a to align contigs to ground truth 109 | print("\nAligning contigs against ground truth...") 110 | sam_file = "{}/contigs_to_truth.sam".format(outdir.rstrip('/')) 111 | subprocess.check_call("bwa index {} 2>/dev/null".format(truth_file), 112 | shell=True) 113 | subprocess.check_call( 114 | "bwa mem -a -L1000 -t 12 {0} {1} > {2} 2>/dev/null".format( 115 | truth_file, contig_file, sam_file), 116 | shell=True) 117 | print() 118 | # score all alignments per contig and select the good alignment(s) 119 | [alignments, unmapped_ids] = read_sam(sam_file) 120 | print("#alignments: {}".format(len(alignments))) 121 | print("#unmapped: {}".format(len(unmapped_ids))) 122 | # score and store alignments 123 | for aln in alignments: 124 | #print aln 125 | [contig_id, ref_id] = aln[0:2] 126 | contig_seq = contigs[contig_id] 127 | truth_seq = ground_truth[ref_id] 128 | [score, aln_range] = score_alignment(aln, contig_seq, truth_seq) 129 | if score[0] <= max_edit: 130 | contigs2aln[contig_id].append([score, aln, aln_range]) 131 | os.remove(sam_file) 132 | 133 | # select optimal alignment(s) 134 | contig_assignments = {} 135 | for contig, aln_list in contigs2aln.items(): 136 | min_score = 1 137 | opt_aln = [] 138 | #print contig 139 | #print aln_list 140 | for i in range(len(aln_list)): 141 | info1 = aln_list[i] 142 | if info1 in opt_aln: 143 | # alignment already paired with another alignment 144 | continue 145 | scores1 = info1[0] 146 | combined = False 147 | for j in range(i+1, len(aln_list)): 148 | info2 = aln_list[j] 149 | # check if these alignments can be combined 150 | if info2[1][1] != info1[1][1]: # not to same ref sequence 151 | continue 152 | elif not (info2[2][1] <= info1[2][0] 153 | or info2[2][0] >= info1[2][1]): 154 | # overlapping alignments 155 | if info2[2][1] >= info1[2][0] and info1[2][1] >= info2[2][0]: 156 | ov_len = info1[2][1]-info2[2][0] 157 | elif info1[2][1] >= info2[2][0] and info2[2][1] >= info1[2][0]: 158 | ov_len = info2[2][1]-info1[2][0] 159 | else: 160 | print("no overlap???") 161 | sys.exit(1) 162 | print("\noverlap of length {} for contig {} on ref {}\n".format(ov_len, contig, info1[1][1])) 163 | ref_len = info1[2][2] 164 | if ov_len > 0.1*ref_len: 165 | print("overlap too long, skipping") 166 | continue 167 | # compute combined score (assigning contig to 2 allele sequences) 168 | combined = True 169 | combined_score = (info1[0][0]*info1[2][2] 170 | + info2[0][0]*info2[2][2]) / (info1[2][2] + info2[2][2]) 171 | # now check if this alignment is optimal 172 | if combined_score == min_score: 173 | ref_id = info1[1][1] 174 | new_aln = True 175 | for aln in opt_aln: 176 | if aln[1][1] != ref_id: 177 | continue 178 | else: 179 | new_aln = False 180 | break 181 | if new_aln: 182 | opt_aln.append(info1) 183 | opt_aln.append(info2) 184 | elif combined_score < min_score: 185 | opt_aln = [info1, info2] 186 | min_score = combined_score 187 | # if there is no pairing, check single alignemnt 188 | if not combined: 189 | scores = scores1 190 | if scores[0] == min_score: 191 | ref_id = info1[1][1] 192 | new_aln = True 193 | for aln in opt_aln: 194 | if aln[1][1] != ref_id: 195 | continue 196 | else: 197 | new_aln = False 198 | break 199 | if new_aln: 200 | opt_aln.append(info1) 201 | elif scores[0] < min_score: 202 | opt_aln = [info1] 203 | min_score = scores[0] 204 | if opt_aln: 205 | contig_assignments[contig] = opt_aln 206 | count = len(opt_aln) 207 | if count > 1: 208 | print("NOTE: contig {} has {} assignments!".format(contig, count)) 209 | return contig_assignments 210 | 211 | 212 | def score_alignment(aln, contig, truth): 213 | [seq_id, ref_id, pos, ori, cigar] = aln 214 | if ori == "-": 215 | contig = revcomp(contig) 216 | # split cigar into numbers and characters all separately 217 | splitcigar = ["".join(x) for _, x in itertools.groupby(cigar, 218 | key=str.isdigit)] 219 | # count mismatches, insertions, deletions and N's 220 | mismatch_count = 0 221 | ins_count = 0 222 | ins_len = 0 223 | del_count = 0 224 | del_len = 0 225 | N_count = 0 226 | length = len(contig) 227 | # keep track of position and cigar index 228 | contig_pos = 0 229 | truth_pos = pos 230 | start_pos = pos 231 | i = 0 232 | while i+1 < len(splitcigar): 233 | aln_len = int(splitcigar[i]) 234 | aln_type = splitcigar[i+1] 235 | if aln_type == "S" or aln_type == "H": 236 | if i == 0: # front end clipped 237 | # if pos > 0: 238 | # del_count += 1 239 | # del_len += pos 240 | clipped_truth = min(aln_len, pos) 241 | length -= aln_len - clipped_truth # correct for overhanging contig length 242 | start_pos -= clipped_truth 243 | contig_pos += aln_len 244 | elif i > 0: # back end clipped 245 | clipped_truth = min(aln_len, len(truth)-truth_pos) 246 | # clipped_truth = len(truth) - truth_pos 247 | # if clipped_truth < 0: 248 | # del_count += 1 249 | # del_len += clipped_truth 250 | length -= aln_len - clipped_truth 251 | contig_pos += aln_len 252 | # truth_pos = len(truth) - clipped_truth 253 | truth_pos += clipped_truth 254 | # compare (partially) aligned sequences 255 | contig_part = contig[contig_pos-clipped_truth : contig_pos] 256 | truth_part = truth[truth_pos-clipped_truth : truth_pos] 257 | [mismatches, Ns] = count_mismatches(contig_part, truth_part) 258 | mismatch_count += mismatches 259 | N_count += Ns 260 | elif aln_type == "I": 261 | if i > 0 and i < len(splitcigar)-2: 262 | ins_count += 1 263 | ins_len += aln_len 264 | N_count += contig[contig_pos:contig_pos+aln_len].count('N') 265 | contig_pos += aln_len 266 | elif aln_type == "D": 267 | del_count += 1 268 | del_len += aln_len 269 | truth_pos += aln_len 270 | elif aln_type == "M": 271 | if contig_pos + aln_len > len(contig): 272 | print("contig {} too short?".format(seq_id)) 273 | print(contig_pos, aln_len, len(contig)) 274 | print(pos, cigar) 275 | if truth_pos + aln_len > len(truth): 276 | print("truth too short?") 277 | print(truth_pos, aln_len, len(truth)) 278 | print(pos, cigar) 279 | contig_part = contig[contig_pos : contig_pos + aln_len] 280 | truth_part = truth[truth_pos : truth_pos + aln_len] 281 | [mismatches, Ns] = count_mismatches(contig_part, truth_part) 282 | mismatch_count += mismatches 283 | N_count += Ns 284 | truth_pos += aln_len 285 | contig_pos += aln_len 286 | else: 287 | print("ERROR: cigar string not recognized.") 288 | sys.exit(1) 289 | i += 2 290 | # compute percent identity 291 | if length > 0: 292 | score = (mismatch_count + ins_len + del_len) / float(length) 293 | else: 294 | score = 0 295 | aln_scores = [score, mismatch_count, ins_count, ins_len, del_count, del_len, 296 | N_count] 297 | assert start_pos >= 0 298 | assert truth_pos <= len(truth) 299 | assert start_pos <= truth_pos 300 | aln_range = [start_pos, truth_pos, len(truth)] 301 | edit_distance = mismatch_count + ins_len + del_len 302 | return [aln_scores, aln_range] 303 | 304 | def count_mismatches(seq1, seq2): 305 | assert len(seq1) == len(seq2) 306 | mismatches = 0 307 | Ns = 0 308 | for i in range(len(seq1)): 309 | if seq1[i] == "N": # truth has N 310 | Ns += 1 311 | elif seq2[i] == "N": # contig has N 312 | Ns += 1 313 | elif seq1[i] != seq2[i]: 314 | mismatches += 1 315 | return [mismatches, Ns] 316 | 317 | 318 | def revcomp(seq): 319 | complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'N': 'N'} 320 | revcomp = "".join(complement.get(base, base) for base in reversed(seq)) 321 | assert len(seq) == len(revcomp) 322 | return revcomp 323 | 324 | 325 | def read_frequencies(freq_truth): 326 | # read true frequencies 327 | true_frequencies = {} 328 | with open(freq_truth, 'r') as f: 329 | for line in f: 330 | [seq_id, freq] = line.strip('\n').split() 331 | true_frequencies[seq_id] = float(freq) 332 | return true_frequencies 333 | 334 | 335 | def read_fasta(filename, read_ab=False): 336 | # returns ID to sequence dict 337 | id2seq = {} 338 | ab_est = {} 339 | with open(filename, 'r') as f: 340 | seq_id = "" 341 | seq = "" 342 | for line in f: 343 | if line[0] == '>': 344 | if seq_id != "" and seq != "": 345 | id2seq[seq_id] = seq 346 | if read_ab: 347 | ab_est[seq_id] = ab # estimated abundance 348 | seq_id = line.lstrip('>').rstrip('\n').split()[0] 349 | if read_ab: 350 | try: 351 | ab = float(line.lstrip('>').rstrip('\n').split()[-1].lstrip('ab=').rstrip('x')) 352 | except ValueError: 353 | print("WARNING: could not read abundance estimates from fasta") 354 | read_ab = False 355 | seq = "" 356 | else: 357 | seq += line.rstrip('\n') 358 | # add final entry 359 | if seq_id != "" and seq != "": 360 | id2seq[seq_id] = seq 361 | if read_ab: 362 | ab_est[seq_id] = ab # estimated abundance 363 | return id2seq, ab_est 364 | 365 | 366 | def read_sam(filename): 367 | # returns a list of all alignments, where each alignment is presented in the 368 | # following format: [seq_id, ref_id, pos, ori, cigar] 369 | aln_list = [] 370 | unmapped_ids = [] 371 | with open(filename, 'r') as f: 372 | for line in f: 373 | if line[0] == "@": 374 | continue 375 | line = line.rstrip('\n').split() 376 | [seq_id, flag, ref_id, pos] = line[0:4] 377 | cigar = line[5] 378 | bits_flag = power_find(int(flag)) 379 | if 4 in bits_flag: # unmapped contig 380 | unmapped_ids.append(seq_id) 381 | continue 382 | elif 16 in bits_flag: # reversed contig 383 | ori = "-" 384 | else: 385 | ori = "+" 386 | aln_list.append([seq_id, ref_id, int(pos)-1, ori, cigar]) # SAM-format is 1-based; switching to 0-based here 387 | return [aln_list, unmapped_ids] 388 | 389 | def power_find(n): 390 | try: 391 | int(n) 392 | except TypeError: 393 | print("power_find TypeError") 394 | print("n = {}".format(n)) 395 | sys.exit(1) 396 | result = [] 397 | binary = bin(n)[:1:-1] 398 | for x in range(len(binary)): 399 | if int(binary[x]): 400 | result.append(2**x) 401 | return result 402 | 403 | 404 | if __name__ == '__main__': 405 | sys.exit(main()) -------------------------------------------------------------------------------- /evaluation/plot_prec_recall.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import sys 3 | import matplotlib.pyplot as plt 4 | import pandas as pd 5 | import numpy as np 6 | import seaborn as sns 7 | import argparse 8 | # 9 | # INDEX = { 10 | # "savage" : 1, 11 | # "virus-vg" : 2, 12 | # "vg-flow": 8, 13 | # "abayesqr" : 5, 14 | # "pehaplo" : 7, 15 | # "predicthaplo" : 3, 16 | # "shorah" : 4 17 | # } 18 | 19 | INDEX = { 20 | "victor" : 1, 21 | "canu" : 2, 22 | "wtdgb2" : 3, 23 | } 24 | 25 | FORMAT = ["method", "contig_id", "truth_id", "aln_len", "edit_dist", 26 | "mismatches", "ins_len", "del_len"] 27 | 28 | def main(): 29 | parser = argparse.ArgumentParser(prog='assembly_evaluation.py', description='Compare assembled contigs to ground truth haplotypes.') 30 | parser.add_argument('contigs', nargs='*', type=str) 31 | parser.add_argument('-a', '--assignments', dest='assignments', type=str, required=True) 32 | parser.add_argument('-n', '--num_strains', dest='num_strains', type=int, required=True) 33 | args = parser.parse_args() 34 | 35 | ref_count = args.num_strains 36 | dist_bins = range(0, 6) 37 | data = pd.read_csv(args.assignments, names=FORMAT,sep="\t") 38 | print(data) 39 | sns.set(style="ticks", context="paper", palette="muted") 40 | fig, axs = plt.subplots(1, 3, sharey=True, figsize=(10, 3), tight_layout=True) 41 | axs[0].set_title("Precision") 42 | axs[1].set_title("Recall") 43 | axs[2].set_title("F-measure") 44 | # print(data["method"].unique()) 45 | for method in args.contigs: 46 | print(method) 47 | method_file = method.split('/')[-1] 48 | method_name = method_file.rstrip(".fa") 49 | color = sns.color_palette()[INDEX[method_name]] 50 | ncontigs = fasta_len(method) 51 | if ncontigs == 0: 52 | continue 53 | subdata = data.loc[data["method"] == method_file] 54 | print(subdata) 55 | stats = [compute_stats(subdata, dist, ref_count, ncontigs) for dist in dist_bins] 56 | precision = [x[0] for x in stats] 57 | recall = [x[1] for x in stats] 58 | f_score = [x[2] for x in stats] 59 | axs[0].plot(dist_bins, precision, label=method_name, color=color) 60 | axs[1].plot(dist_bins, recall, label=method_name, color=color) 61 | axs[2].plot(dist_bins, f_score, label=method_name, color=color) 62 | for ax in axs: 63 | ax.set_xticks(dist_bins) 64 | ax.set_xlabel("max % edit distance") 65 | plt.legend(loc='upper center', bbox_to_anchor=(1.35, 0.75), shadow=True, ncol=1) 66 | plt.savefig('prec_recall_fscore.png') 67 | plt.show() 68 | return 69 | 70 | 71 | def compute_stats(data, max_edit_perc, ref_count, ncontigs): 72 | true_positives = set() 73 | matched_ref = set() 74 | for index, record in data.iterrows(): 75 | print(record) 76 | edit_perc = record['edit_dist'] / record['aln_len'] * 100 77 | if edit_perc <= max_edit_perc: 78 | true_positives.add(record['contig_id']) 79 | matched_ref.add(record['truth_id']) 80 | print(matched_ref) 81 | recall = len(matched_ref) / ref_count 82 | precision = len(true_positives) / ncontigs if ncontigs > 0 else 0 83 | if precision + recall > 0: 84 | f_score = 2 * precision * recall / (precision + recall) 85 | else: 86 | f_score = 0 87 | print(precision,recall,f_score) 88 | return precision, recall, f_score 89 | 90 | 91 | def fasta_len(fname): 92 | with open(fname) as f: 93 | i = 0 94 | for line in f: 95 | if line[0] == '>': 96 | i += 1 97 | return i 98 | 99 | 100 | if __name__ == '__main__': 101 | sys.exit(main()) 102 | -------------------------------------------------------------------------------- /evaluation/plot_prec_recall_av.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import sys 3 | import matplotlib.pyplot as plt 4 | import pandas as pd 5 | import numpy as np 6 | import seaborn as sns 7 | import argparse 8 | 9 | INDEX = { 10 | "savage" : 1, 11 | "virus-vg" : 2, 12 | "abayesqr" : 5, 13 | "pehaplo" : 7, 14 | "predicthaplo" : 3, 15 | "shorah" : 4 16 | } 17 | 18 | FORMAT = ["method", "contig_id", "truth_id", "aln_len", "edit_dist", 19 | "mismatches", "ins_len", "del_len"] 20 | 21 | def main(): 22 | parser = argparse.ArgumentParser(prog='assembly_evaluation.py', description='Compare assembled contigs to ground truth haplotypes.') 23 | # parser.add_argument('contigs', nargs='*', type=str) 24 | parser.add_argument('-a', '--assignments', dest='assignments', type=str, required=True) 25 | parser.add_argument('-n', '--num_strains', dest='num_strains', type=int, required=True) 26 | parser.add_argument('-c', '--cov_list', dest='cov_list', type=str, required=True) 27 | args = parser.parse_args() 28 | 29 | ref_count = args.num_strains 30 | dist_bins = range(0, 6) 31 | data = pd.read_csv(args.assignments, names=FORMAT,sep="\t") 32 | 33 | # print(data) 34 | sns.set(style="ticks", context="paper", palette="muted") 35 | # print(data["method"].unique()) 36 | for cov in args.cov_list.split(','): 37 | print("{}x".format(cov)) 38 | fig, axs = plt.subplots(1, 3, sharey=True, figsize=(10, 3), tight_layout=True) 39 | axs[0].set_title("Precision") 40 | axs[1].set_title("Recall") 41 | axs[2].set_title("F-measure") 42 | for method_name in INDEX.keys(): 43 | print(method_name) 44 | color = sns.color_palette()[INDEX[method_name]] 45 | prec_list = [] 46 | recall_list = [] 47 | f_score_list = [] 48 | for sample in range(1, 11): 49 | filename = "{}/sample{}.{}x.fasta".format(method_name, sample, cov) 50 | try: 51 | ncontigs = int(fasta_len(filename)) 52 | except FileNotFoundError as e: 53 | continue 54 | if ncontigs == 0: 55 | continue 56 | subdata = data.loc[data["method"] == filename] 57 | # print(subdata) 58 | stats = [compute_stats(subdata, dist, ref_count, ncontigs) for dist in dist_bins] 59 | prec_list.append([x[0] for x in stats]) 60 | recall_list.append([x[1] for x in stats]) 61 | f_score_list.append([x[2] for x in stats]) 62 | nsamples = len(prec_list) 63 | if nsamples == 0: 64 | continue 65 | precision = np.mean(np.array(prec_list), axis=0) 66 | prec_std = np.std(np.array(prec_list), axis=0) 67 | recall = np.mean(np.array(recall_list), axis=0) 68 | recall_std = np.std(np.array(recall_list), axis=0) 69 | f_score = np.mean(np.array(f_score_list), axis=0) 70 | f_score_std = np.std(np.array(f_score_list), axis=0) 71 | # now plot these averages with error bars 72 | axs[0].errorbar(dist_bins, precision, yerr=prec_std, capsize=3, 73 | label=method_name, color=color) 74 | axs[1].errorbar(dist_bins, recall, yerr=recall_std, capsize=3, 75 | label=method_name, color=color) 76 | axs[2].errorbar(dist_bins, f_score, yerr=f_score_std, capsize=3, 77 | label=method_name, color=color) 78 | for ax in axs: 79 | ax.set_xticks(dist_bins) 80 | ax.set_xlabel("max % edit distance") 81 | plt.legend(loc='upper center', bbox_to_anchor=(1.35, 0.75), shadow=True, ncol=1) 82 | plt.savefig('prec_recall_fscore.{}x.eps'.format(cov)) 83 | plt.show() 84 | return 85 | 86 | 87 | def compute_stats(data, max_edit_perc, ref_count, ncontigs): 88 | true_positives = set() 89 | matched_ref = set() 90 | for index, record in data.iterrows(): 91 | # print(record) 92 | edit_perc = record['edit_dist'] / record['aln_len'] * 100 93 | if edit_perc <= max_edit_perc: 94 | true_positives.add(record['contig_id']) 95 | matched_ref.add(record['truth_id']) 96 | recall = len(matched_ref) / ref_count 97 | precision = len(true_positives) / ncontigs if ncontigs > 0 else 0 98 | if precision + recall > 0: 99 | f_score = 2 * precision * recall / (precision + recall) 100 | else: 101 | f_score = 0 102 | return precision, recall, f_score 103 | 104 | 105 | def fasta_len(fname): 106 | with open(fname) as f: 107 | i = 0 108 | for line in f: 109 | if line[0] == '>': 110 | i += 1 111 | return i 112 | 113 | 114 | if __name__ == '__main__': 115 | sys.exit(main()) 116 | -------------------------------------------------------------------------------- /example/ref.fa: -------------------------------------------------------------------------------- 1 | >HXB2 2 | TGGAAGGGCTAATTCACTCCCAAAGAAGACAAGATATCCTTGATCTGTGGATCTACCACACACAAGGCTACTTCCCTGATTAGCAGAACTACACACCAGGGCCAGGGGTCAGATATCCACTGACCTTTGGATGGTGCTACAAGCTAGTACCAGTTGAGCCAGAGAAGGTAGAAGAAGCCAATAAAGGAGAGAACACCAGCTTGTTACACCCTGTGAGCCTGCATGGGATGGATGACCCGGAGAGAGAAGTGTTAGAGTGGAGGTTTGACAGCCGCCTAGCATTTCATCACGTGGCCCGAGAGCTGCATCCGGAGTACTTCAAGAACTGCTGATATCGAGCTTGCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGCGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATCCTGCATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCTGGTTAGACCAGATCTGAGCCTGGGAGCTCTCTGGCTAACTAGGGAACCCACTGCTTAAGCCTCAATAAAGCTTGCCTTGAGTGCTTCAAGTAGTGTGTGCCCGTCTGTTGTGTGACTCTGGTAACTAGAGATCCCTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCAGTGGCGCCCGAACAGGGACTTGAAAGCGAAAGGGAAACCAGAGGAGCTCTCTCGACGCAGGACTCGGCTTGCTGAAGCGCGCACGGCAAGAGGCGAGGGGCGGCGACTGGTGAGTACGCCAAAAATTTTGACTAGCGGAGGCTAGAAGGAGAGAGATGGGTGCGAGAGCGTCAGTATTAAGCGGGGGAGAATTAGATCGATGGGAAAAAATTCGGTTAAGGCCAGGGGGAAAGAAAAAATATAAATTAAAACATATAGTATGGGCAAGCAGGGAGCTAGAACGATTCGCAGTTAATCCTGGCCTGTTAGAAACATCAGAAGGCTGTAGACAAATACTGGGACAGCTACAACCATCCCTTCAGACAGGATCAGAAGAACTTAGATCATTATATAATACAGTAGCAACCCTCTATTGTGTGCATCAAAGGATAGAGATAAAAGACACCAAGGAAGCTTTAGACAAGATAGAGGAAGAGCAAAACAAAAGTAAGAAAAAAGCACAGCAAGCAGCAGCTGACACAGGACACAGCAATCAGGTCAGCCAAAATTACCCTATAGTGCAGAACATCCAGGGGCAAATGGTACATCAGGCCATATCACCTAGAACTTTAAATGCATGGGTAAAAGTAGTAGAAGAGAAGGCTTTCAGCCCAGAAGTGATACCCATGTTTTCAGCATTATCAGAAGGAGCCACCCCACAAGATTTAAACACCATGCTAAACACAGTGGGGGGACATCAAGCAGCCATGCAAATGTTAAAAGAGACCATCAATGAGGAAGCTGCAGAATGGGATAGAGTGCATCCAGTGCATGCAGGGCCTATTGCACCAGGCCAGATGAGAGAACCAAGGGGAAGTGACATAGCAGGAACTACTAGTACCCTTCAGGAACAAATAGGATGGATGACAAATAATCCACCTATCCCAGTAGGAGAAATTTATAAAAGATGGATAATCCTGGGATTAAATAAAATAGTAAGAATGTATAGCCCTACCAGCATTCTGGACATAAGACAAGGACCAAAAGAACCCTTTAGAGACTATGTAGACCGGTTCTATAAAACTCTAAGAGCCGAGCAAGCTTCACAGGAGGTAAAAAATTGGATGACAGAAACCTTGTTGGTCCAAAATGCGAACCCAGATTGTAAGACTATTTTAAAAGCATTGGGACCAGCGGCTACACTAGAAGAAATGATGACAGCATGTCAGGGAGTAGGAGGACCCGGCCATAAGGCAAGAGTTTTGGCTGAAGCAATGAGCCAAGTAACAAATTCAGCTACCATAATGATGCAGAGAGGCAATTTTAGGAACCAAAGAAAGATTGTTAAGTGTTTCAATTGTGGCAAAGAAGGGCACACAGCCAGAAATTGCAGGGCCCCTAGGAAAAAGGGCTGTTGGAAATGTGGAAAGGAAGGACACCAAATGAAAGATTGTACTGAGAGACAGGCTAATTTTTTAGGGAAGATCTGGCCTTCCTACAAGGGAAGGCCAGGGAATTTTCTTCAGAGCAGACCAGAGCCAACAGCCCCACCAGAAGAGAGCTTCAGGTCTGGGGTAGAGACAACAACTCCCCCTCAGAAGCAGGAGCCGATAGACAAGGAACTGTATCCTTTAACTTCCCTCAGATCACTCTTTGGCAACGACCCCTCGTCACAATAAAGATAGGGGGGCAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAGAAGAAATGAGTTTGCCAGGAAGATGGAAACCAAAAATGATAGGGGGAATTGGAGGTTTTATCAAAGTAAGACAGTATGATCAGATACTCATAGAAATCTGTGGACATAAAGCTATAGGTACAGTATTAGTAGGACCTACACCTGTCAACATAATTGGAAGAAATCTGTTGACTCAGATTGGTTGCACTTTAAATTTTCCCATTAGCCCTATTGAGACTGTACCAGTAAAATTAAAGCCAGGAATGGATGGCCCAAAAGTTAAACAATGGCCATTGACAGAAGAAAAAATAAAAGCATTAGTAGAAATTTGTACAGAGATGGAAAAGGAAGGGAAAATTTCAAAAATTGGGCCTGAAAATCCATACAATACTCCAGTATTTGCCATAAAGAAAAAAGACAGTACTAAATGGAGAAAATTAGTAGATTTCAGAGAACTTAATAAGAGAACTCAAGACTTCTGGGAAGTTCAATTAGGAATACCACATCCCGCAGGGTTAAAAAAGAAAAAATCAGTAACAGTACTGGATGTGGGTGATGCATATTTTTCAGTTCCCTTAGATGAAGACTTCAGGAAATATACTGCATTTACCATACCTAGTATAAACAATGAGACACCAGGGATTAGATATCAGTACAATGTGCTTCCACAGGGATGGAAAGGATCACCAGCAATATTCCAAAGTAGCATGACAAAAATCTTAGAGCCTTTTAGAAAACAAAATCCAGACATAGTTATCTATCAATACATGGATGATTTGTATGTAGGATCTGACTTAGAAATAGGGCAGCATAGAACAAAAATAGAGGAGCTGAGACAACATCTGTTGAGGTGGGGACTTACCACACCAGACAAAAAACATCAGAAAGAACCTCCATTCCTTTGGATGGGTTATGAACTCCATCCTGATAAATGGACAGTACAGCCTATAGTGCTGCCAGAAAAAGACAGCTGGACTGTCAATGACATACAGAAGTTAGTGGGGAAATTGAATTGGGCAAGTCAGATTTACCCAGGGATTAAAGTAAGGCAATTATGTAAACTCCTTAGAGGAACCAAAGCACTAACAGAAGTAATACCACTAACAGAAGAAGCAGAGCTAGAACTGGCAGAAAACAGAGAGATTCTAAAAGAACCAGTACATGGAGTGTATTATGACCCATCAAAAGACTTAATAGCAGAAATACAGAAGCAGGGGCAAGGCCAATGGACATATCAAATTTATCAAGAGCCATTTAAAAATCTGAAAACAGGAAAATATGCAAGAATGAGGGGTGCCCACACTAATGATGTAAAACAATTAACAGAGGCAGTGCAAAAAATAACCACAGAAAGCATAGTAATATGGGGAAAGACTCCTAAATTTAAACTGCCCATACAAAAGGAAACATGGGAAACATGGTGGACAGAGTATTGGCAAGCCACCTGGATTCCTGAGTGGGAGTTTGTTAATACCCCTCCTTTAGTGAAATTATGGTACCAGTTAGAGAAAGAACCCATAGTAGGAGCAGAAACCTTCTATGTAGATGGGGCAGCTAACAGGGAGACTAAATTAGGAAAAGCAGGATATGTTACTAATAGAGGAAGACAAAAAGTTGTCACCCTAACTGACACAACAAATCAGAAGACTGAGTTACAAGCAATTTATCTAGCTTTGCAGGATTCGGGATTAGAAGTAAACATAGTAACAGACTCACAATATGCATTAGGAATCATTCAAGCACAACCAGATCAAAGTGAATCAGAGTTAGTCAATCAAATAATAGAGCAGTTAATAAAAAAGGAAAAGGTCTATCTGGCATGGGTACCAGCACACAAAGGAATTGGAGGAAATGAACAAGTAGATAAATTAGTCAGTGCTGGAATCAGGAAAGTACTATTTTTAGATGGAATAGATAAGGCCCAAGATGAACATGAGAAATATCACAGTAATTGGAGAGCAATGGCTAGTGATTTTAACCTGCCACCTGTAGTAGCAAAAGAAATAGTAGCCAGCTGTGATAAATGTCAGCTAAAAGGAGAAGCCATGCATGGACAAGTAGACTGTAGTCCAGGAATATGGCAACTAGATTGTACACATTTAGAAGGAAAAGTTATCCTGGTAGCAGTTCATGTAGCCAGTGGATATATAGAAGCAGAAGTTATTCCAGCAGAAACAGGGCAGGAAACAGCATATTTTCTTTTAAAATTAGCAGGAAGATGGCCAGTAAAAACAATACATACAGACAATGGCAGCAATTTCACCAGTGCTACGGTTAAGGCCGCCTGTTGGTGGGCGGGAATCAAGCAGGAATTTGGAATTCCCTACAATCCCCAAAGTCAAGGAGTAGTAGAATCTATGAATAAAGAATTAAAGAAAATTATAGGACAGGTAAGAGATCAGGCTGAACATCTTAAGACAGCAGTACAAATGGCAGTATTCATCCACAATTTTAAAAGAAAAGGGGGGATTGGGGGGTACAGTGCAGGGGAAAGAATAGTAGACATAATAGCAACAGACATACAAACTAAAGAATTACAAAAACAAATTACAAAAATTCAAAATTTTCGGGTTTATTACAGGGACAGCAGAAATCCACTTTGGAAAGGACCAGCAAAGCTCCTCTGGAAAGGTGAAGGGGCAGTAGTAATACAAGATAATAGTGACATAAAAGTAGTGCCAAGAAGAAAAGCAAAGATCATTAGGGATTATGGAAAACAGATGGCAGGTGATGATTGTGTGGCAAGTAGACAGGATGAGGATTAGAACATGGAAAAGTTTAGTAAAACACCATATGTATGTTTCAGGGAAAGCTAGGGGATGGTTTTATAGACATCACTATGAAAGCCCTCATCCAAGAATAAGTTCAGAAGTACACATCCCACTAGGGGATGCTAGATTGGTAATAACAACATATTGGGGTCTGCATACAGGAGAAAGAGACTGGCATTTGGGTCAGGGAGTCTCCATAGAATGGAGGAAAAAGAGATATAGCACACAAGTAGACCCTGAACTAGCAGACCAACTAATTCATCTGTATTACTTTGACTGTTTTTCAGACTCTGCTATAAGAAAGGCCTTATTAGGACACATAGTTAGCCCTAGGTGTGAATATCAAGCAGGACATAACAAGGTAGGATCTCTACAATACTTGGCACTAGCAGCATTAATAACACCAAAAAAGATAAAGCCACCTTTGCCTAGTGTTACGAAACTGACAGAGGATAGATGGAACAAGCCCCAGAAGACCAAGGGCCACAGAGGGAGCCACACAATGAATGGACACTAGAGCTTTTAGAGGAGCTTAAGAATGAAGCTGTTAGACATTTTCCTAGGATTTGGCTCCATGGCTTAGGGCAACATATCTATGAAACTTATGGGGATACTTGGGCAGGAGTGGAAGCCATAATAAGAATTCTGCAACAACTGCTGTTTATCCATTTTCAGAATTGGGTGTCGACATAGCAGAATAGGCGTTACTCGACAGAGGAGAGCAAGAAATGGAGCCAGTAGATCCTAGACTAGAGCCCTGGAAGCATCCAGGAAGTCAGCCTAAAACTGCTTGTACCAATTGCTATTGTAAAAAGTGTTGCTTTCATTGCCAAGTTTGTTTCATAACAAAAGCCTTAGGCATCTCCTATGGCAGGAAGAAGCGGAGACAGCGACGAAGAGCTCATCAGAACAGTCAGACTCATCAAGCTTCTCTATCAAAGCAGTAAGTAGTACATGTAACGCAACCTATACCAATAGTAGCAATAGTAGCATTAGTAGTAGCAATAATAATAGCAATAGTTGTGTGGTCCATAGTAATCATAGAATATAGGAAAATATTAAGACAAAGAAAAATAGACAGGTTAATTGATAGACTAATAGAAAGAGCAGAAGACAGTGGCAATGAGAGTGAAGGAGAAATATCAGCACTTGTGGAGATGGGGGTGGAGATGGGGCACCATGCTCCTTGGGATGTTGATGATCTGTAGTGCTACAGAAAAATTGTGGGTCACAGTCTATTATGGGGTACCTGTGTGGAAGGAAGCAACCACCACTCTATTTTGTGCATCAGATGCTAAAGCATATGATACAGAGGTACATAATGTTTGGGCCACACATGCCTGTGTACCCACAGACCCCAACCCACAAGAAGTAGTATTGGTAAATGTGACAGAAAATTTTAACATGTGGAAAAATGACATGGTAGAACAGATGCATGAGGATATAATCAGTTTATGGGATCAAAGCCTAAAGCCATGTGTAAAATTAACCCCACTCTGTGTTAGTTTAAAGTGCACTGATTTGAAGAATGATACTAATACCAATAGTAGTAGCGGGAGAATGATAATGGAGAAAGGAGAGATAAAAAACTGCTCTTTCAATATCAGCACAAGCATAAGAGGTAAGGTGCAGAAAGAATATGCATTTTTTTATAAACTTGATATAATACCAATAGATAATGATACTACCAGCTATAAGTTGACAAGTTGTAACACCTCAGTCATTACACAGGCCTGTCCAAAGGTATCCTTTGAGCCAATTCCCATACATTATTGTGCCCCGGCTGGTTTTGCGATTCTAAAATGTAATAATAAGACGTTCAATGGAACAGGACCATGTACAAATGTCAGCACAGTACAATGTACACATGGAATTAGGCCAGTAGTATCAACTCAACTGCTGTTAAATGGCAGTCTAGCAGAAGAAGAGGTAGTAATTAGATCTGTCAATTTCACGGACAATGCTAAAACCATAATAGTACAGCTGAACACATCTGTAGAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGAATCCGTATCCAGAGAGGACCAGGGAGAGCATTTGTTACAATAGGAAAAATAGGAAATATGAGACAAGCACATTGTAACATTAGTAGAGCAAAATGGAATAACACTTTAAAACAGATAGCTAGCAAATTAAGAGAACAATTTGGAAATAATAAAACAATAATCTTTAAGCAATCCTCAGGAGGGGACCCAGAAATTGTAACGCACAGTTTTAATTGTGGAGGGGAATTTTTCTACTGTAATTCAACACAACTGTTTAATAGTACTTGGTTTAATAGTACTTGGAGTACTGAAGGGTCAAATAACACTGAAGGAAGTGACACAATCACCCTCCCATGCAGAATAAAACAAATTATAAACATGTGGCAGAAAGTAGGAAAAGCAATGTATGCCCCTCCCATCAGTGGACAAATTAGATGTTCATCAAATATTACAGGGCTGCTATTAACAAGAGATGGTGGTAATAGCAACAATGAGTCCGAGATCTTCAGACCTGGAGGAGGAGATATGAGGGACAATTGGAGAAGTGAATTATATAAATATAAAGTAGTAAAAATTGAACCATTAGGAGTAGCACCCACCAAGGCAAAGAGAAGAGTGGTGCAGAGAGAAAAAAGAGCAGTGGGAATAGGAGCTTTGTTCCTTGGGTTCTTGGGAGCAGCAGGAAGCACTATGGGCGCAGCGTCAATGACGCTGACGGTACAGGCCAGACAATTATTGTCTGGTATAGTGCAGCAGCAGAACAATTTGCTGAGGGCTATTGAGGCGCAACAGCATCTGTTGCAACTCACAGTCTGGGGCATCAAGCAGCTCCAGGCAAGAATCCTGGCTGTGGAAAGATACCTAAAGGATCAACAGCTCCTGGGGATTTGGGGTTGCTCTGGAAAACTCATTTGCACCACTGCTGTGCCTTGGAATGCTAGTTGGAGTAATAAATCTCTGGAACAGATTTGGAATCACACGACCTGGATGGAGTGGGACAGAGAAATTAACAATTACACAAGCTTAATACACTCCTTAATTGAAGAATCGCAAAACCAGCAAGAAAAGAATGAACAAGAATTATTGGAATTAGATAAATGGGCAAGTTTGTGGAATTGGTTTAACATAACAAATTGGCTGTGGTATATAAAATTATTCATAATGATAGTAGGAGGCTTGGTAGGTTTAAGAATAGTTTTTGCTGTACTTTCTATAGTGAATAGAGTTAGGCAGGGATATTCACCATTATCGTTTCAGACCCACCTCCCAACCCCGAGGGGACCCGACAGGCCCGAAGGAATAGAAGAAGAAGGTGGAGAGAGAGACAGAGACAGATCCATTCGATTAGTGAACGGATCCTTGGCACTTATCTGGGACGATCTGCGGAGCCTGTGCCTCTTCAGCTACCACCGCTTGAGAGACTTACTCTTGATTGTAACGAGGATTGTGGAACTTCTGGGACGCAGGGGGTGGGAAGCCCTCAAATATTGGTGGAATCTCCTACAGTATTGGAGTCAGGAACTAAAGAATAGTGCTGTTAGCTTGCTCAATGCCACAGCCATAGCAGTAGCTGAGGGGACAGATAGGGTTATAGAAGTAGTACAAGGAGCTTGTAGAGCTATTCGCCACATACCTAGAAGAATAAGACAGGGCTTGGAAAGGATTTTGCTATAAGATGGGTGGCAAGTGGTCAAAAAGTAGTGTGATTGGATGGCCTACTGTAAGGGAAAGAATGAGACGAGCTGAGCCAGCAGCAGATAGGGTGGGAGCAGCATCTCGAGACCTGGAAAAACATGGAGCAATCACAAGTAGCAATACAGCAGCTACCAATGCTGCTTGTGCCTGGCTAGAAGCACAAGAGGAGGAGGAGGTGGGTTTTCCAGTCACACCTCAGGTACCTTTAAGACCAATGACTTACAAGGCAGCTGTAGATCTTAGCCACTTTTTAAAAGAAAAGGGGGGACTGGAAGGGCTAATTCACTCCCAAAGAAGACAAGATATCCTTGATCTGTGGATCTACCACACACAAGGCTACTTCCCTGATTAGCAGAACTACACACCAGGGCCAGGGGTCAGATATCCACTGACCTTTGGATGGTGCTACAAGCTAGTACCAGTTGAGCCAGATAAGGTAGAAGAGGCCAATAAAGGAGAGAACACCAGCTTGTTACACCCTGTGAGCCTGCATGGGATGGATGACCCGGAGAGAGAAGTGTTAGAGTGGAGGTTTGACAGCCGCCTAGCATTTCATCACGTGGCCCGAGAGCTGCATCCGGAGTACTTCAAGAACTGCTGATATCGAGCTTGCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGCGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATCCTGCATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCTGGTTAGACCAGATCTGAGCCTGGGAGCTCTCTGGCTAACTAGGGAACCCACTGCTTAAGCCTCAATAAAGCTTGCCTTGAGTGCTTCAAGTAGTGTGTGCCCGTCTGTTGTGTGACTCTGGTAACTAGAGATCCCTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCA 3 | >JRCSF 4 | AGGGCTAATTTACTCACAGAAAAGACAAGATATCCTTGATCTGTGGATCTACCACACACAAGGCTTCTTCCCTGATTGGCAGAACTACACAGCAGGACCAGGGGTCAGATTTCCACTGACCTTTGGATGGTGCTTCAAGCTAGTACCAGTTGATCCAGAGAAGGTAGAAGAGGCCAATGAAGGAGAGAACAACTGCTTGTTACACCCTATGAGCCAGCATGGAATGGACGACCCAGAGAAGGAAGTGTTAGTGTGGAAGTTTGACAGCAAGCTAGCATTGCATCACGTGGCCCGAGAGCTGCATCCGGAGTACTACAAGGACTGCTGACACCGAGCTTTCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGCGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATGCTGCATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCTGGTTAGACCAGATCTGAGCCTGGGAGCTCTCTGGCTAGCTAGGGAACCCACTGCTTAAGCCTCAATAAAGCTTGCCTTGAGTGCTTCAAGTAGTGTGTGCCCGTCTGTTGTGTGACTCTGGTAACTAGAGATCCCTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCAGTGGCGCCCGAACAGGGACCGGAAAGCGAAAGAGAAACCAGAGGAGATCTCTCGACGCAGGACTCGGCTTGCTGAAGCGCGCACAGCAAGAGGCGAGGGGCGGCGACTGGTGAGTACGCCGAAATTTTGACTAGCGGAGGCTAGAAGGAGAGAGATGGGTGCGAGAGCGTCAGTATTAAGCGGGGGAGAATTGGATAGGTGGGAAAAAATTCGGTTAAGGCCAGGAGGAAAGAAAAAATATAGATTAAAACATATAGTATGGGCAAGCAGGGAGCTAGAACGTTTCGCAGTCAATCCTGGCCTGTTAGAATCATCAGAAGGCTGTAGACAAATACTGGGACAACTACAACCATCCCTTAAGACAGGATCAGAAGAACTTACATCATTATATAATACAGTAGCAACCCTCTATTGTGTACATCAAAGGATAGAGATAAAAGACACCAAGGAAGCTTTAGAAAAGATAGAGGAAGAGCAAACCAAAAGTATGAAAAAGGCACAGCAAGCAGCAGCTGACACAGGAAACAGCAGCCAGGTCAGCCAAAATTACCCTATAGTGCAGAACCTGCAGGGGCAAATGGTACATCAGGCCATATCACCTAGAACTTTAAATGCATGGGTAAAAGTAATAGAAGAGAAGGCTTTCAGCCCCGAAGTAATACCCATGTTTTCAGCATTATCAGAAGGAGCCACCCCACAAGATTTAAACACCATGCTAAACACAGTGGGGGGACATCAAGCAGCTATGCAAATGCTAAAAGAAACCATCAATGAGGAAGCTGCAGAATGGGATAGATTGCATCCAGTGCATGCAGGGCCTATTGCACCAGGCCAGATGAGAGAACCAAGGGGAAGTGACATAGCAGGGACTACTAGTACCCTTCAGGAACAAATAGGATGGATGACAAATAATCCACCTATCCCAGTAGGAGAAATCTATAAAAGATGGATAATCCTGGGGTTAAATAAAATAGTAAGGATGTATAGCCCTGTCAGCATTCTGGACATAAGACAAGGACCAAAGGAACCCTTTAGAGACTATGTAGACCGGTTCTATAAAACCCTAAGAGCCGAGCAAGCTACACAGGAGGTAAAAAATTGGATGACAGAAACCTTGTTGGTCCAAAATGCGAACCCAGATTGTAAAACTATTTTAAAAGCATTGGGACCAGCAGCTACACTAGAAGAAATGATGACAGCATGTCAGGGAGTGGGAGGACCCGGCCATAAAGCAAGAGTTTTGGCTGAAGCAATGAGCCAAGTAACAAATCCAGCTACCATAATGATGCAGAGAGGCAACTTTAGGAACCAAAGAAAGAATGTTAAGTGTTTCAATTGTGGCAAAGAAGGGCACATAGCCAGAAATTGCAGGGCCCCTAGGAAAAAGGGCTGTTGGAAATGTGGAAAGGAAGGACACCAAATGAAAGAGTGTACTGAGAGACAGGCTAATTTTTTAGGGAAGATCTGGCCTTCCTACAAGGGAAGGCCAGGGAATTTCCTTCAGAGCAGACCAGAGCCAACAGCCCCACCAGAAGAGAGCTTCAGGTTTGGGGAAGAGACAGCAACTCCCTCTCAGAAGCAGGAGCAGAAGCAGGAGCCGATAGACAAGGAATTGTATCCTTTAACTTCCCTCAGATCACTCTTTGGCAACGACCCCTCGTCACAATAAAGATAGGGGGGCAACTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAGAAGACATGGATTTGCCAGGAAGATGGAAACCAAAAATGATAGGGGGAATTGGAGGTTTTATCAAAGTAAGACAGTATGATCAGATACCCATAGATATCTGTGGACATAAAGCTGTAGGTACAGTATTAGTAGGACCTACACCTGTCAACATAATTGGAAGAAATCTGTTGACTCAGATTGGTTGCACTTTAAATTTTCCCATTAGTCCTATTGAAACTGTACCAGTAAAATTAAAGCCAGGAATGGATGGCCCAAAAGTCAAACAATGGCCATTGACAGAAGAAAAAATAAAAGCATTAGTAGAAATTTGTACAGAAATGGAAAAGGAAGGAAAGATTTCAAAAATTGGGCCTGAAAATCCATACAATACTCCAGTATTTGCCATAAAGAAAAAAGACAGTACTAAATGGAGAAAATTAGTAGATTTCAGAGAACTTAATAGGAGAACTCAAGACTTCTGGGAAGTTCAATTAGGAATACCACATCCCGCAGGGTTAAAAAAGAAAAAATCAGTAACAGTACTGGATGTGGGTGATGCATATTTTTCAGTTCCCTTAGATAAAGACTTCAGGAAGTATACTGCATTTACCATACCTAGTATAAACAATGAGACACCAGGGATTAGATATCAGTACAATGTGCTTCCACAGGGATGGAAAGGATCACCAGCAATATTCCAAAGTAGCATGACAAAAATCTTAGAGCCTTTTAGAAAACAAAATCCAGACATAATTATCTATCAATACATGGATGATTTGTATGTAGGATCTGACTTAGAAATAGGGCAGCATAGAACAAAAATAGAGGAACTGAGACAACATCTGTTGAAGTGGGGATTTACCACACCAGACAAAAAACATCAGAAAGAACCTCCATTCCTTTGGATGGGTTATGAACTCCATCCTGATAAATGGACAGTACAGCCTATAGTGCTGCCAGAAAAAGACAGCTGGACTGTCAATGACATACAGAAGTTAGTGGGAAAATTGAATTGGGCAAGTCAAATTTATGCAGGGATTAAAGTAAAGCAATTATGTAAACTCCTTAGGGGAACCAAAGCACTTACAGAAGTAATACCACTAACAAAAGAAGCAGAGCTAGAACTGGCAGAAAACAGGGAGATTCTAAAAGAACCAGTACATGGAGTGTATTATGACCCATCAAAAGACTTAATAGTAGAAATACAGAAGCAGGGGCAAGGCCAATGGACATATCAAATTTTTCAAGAGCCATTTAAAAATCTGAAAACAGGAAAATATGCAAGAACGAGGGGTGCCCACACTAATGATGTAAAACAATTAACAGAGGCAGTGCAAAAAATAGCCAATGAAAGCATAGTAATATGGGGAAAGATTCCTAAATTTAAATTACCCATACAAAAAGAAACATGGGAAACATGGTGGACAGAGTATTGGCAAGCCACCTGGATTCCTGAGTGGGAGTTTGTCAATACCCCTCCCTTAGTGAAATTATGGTACCAGTTAGAAAAAGAACCCATAGTAGGAGCAGAAACTTTCTATGTAGATGGGGCAGCTAACAGGGAGACTAAATTAGGAAAAGCAGGATATGTTACTAGCAGAGGAAGACAAAAAGTTGTCTCCCTAACAGACACAACAAATCAGAAAACTGAGTTACAAGCAATTCACCTAGCTTTGCAGGATTCAGGATTAGAAGTAAACATAGTAACAGACTCACAATATGCATTAGGAATCATTCAAGCACAACCAGATAAAAGTGAATCAGAGTTAGTCAGTCAAATAATAGAACAGCTAATAAAAAAGGAAAAAGTCTACCTGGCATGGGTACCAGCACACAAAGGAATTGGAGGAAATGAACAGGTAGATAAATTAGTCAGTGCTGGAATCAGGAAAGTGCTATTTTTAGATGGAATAGATAAGGCCCAAGAAGATCATGAAAAATATCACAGTAATTGGAGAGCAATGGCTAGTGATTTTAACCTGCCACCTATAGTAGCAAAAGAAATAGTAGCCAGCTGTGATAAATGTCAGCTAAAAGGAGAAGCCATGCATGGACAAGTAGACTGTAGTCCAGGAATATGGCAACTAGATTGTACACATTTAGAAGGAAAAATTATCCTGGTAGCAGTTCATGTAGCCAGTGGATATATAGAAGCAGAAGTTATTCCAGCAGAAACAGGGCAGGAAACAGCATACTTTCTCTTAAAATTAGCAGGCAGATGGCCAGTAACAACAATACATACAGACAATGGCAGCAATTTCACCAGTACTACAGTTAAGGCCGCCTGTTGGTGGGCTGGGATCAAGCAGGAATTTGGCATTCCCTACAATCCCCAAAGTCAAGGAGTAGTAGAATCTATGAATAAAGAATTAAAGAAAATTATAGGACAGGTAAGAGATCAGGCTGAACATCTTAAGACAGCAGTACAAATGGCAGTATTCATCCACAATTTTAAAAGAAAAGGGGGGATTGGGGGGTACAGTGCAGGGGAAAGAATAATAGACATAATAGCAACAGACATACAAACTAAAGAATTACAAAAACAAATTACAAAAATTCAAAATTTTCGGGTTTATTACAGGGACAACAGAGATCCAATTTGGAAAGGACCAGCAAAGCTTCTCTGGAAAGGTGAAGGGGCAGTAGTAATACAAGATAATAGTGACATAAAAGTAGTGCCAAGAAGAAAAGTAAAAATCATTAGGGATTATGGAAAACAGATGGCAGGTGATGATTGTGTGGCAAGTAGACAGGATGAGGATTAGAACATGGAACAGTTTAGTAAAACACCATATGTATATTTCAGGGAAAGCTAAGGGATGGATTTATAAACATCACTATGAAAGCACTAATCCAAGAGTAAGTTCAGAAGTACAAATCCCACTAGGGGATGCTAGATTGGTAATAACAACATATTGGGGTCTGCATACAGGAGAAAGAGACTGGCATTTGGGTCAGGGAGTCTCCATGGAATGGAGGACAAGGAGATATAGCACACAAGTAGACCCTGACCTAGCAGACCAACTAATTCATCTGTATTACTTTGATTGTTTTTCAGAATCTGCTATAAGGAATGCCATATTAGGACATATAGTTAGTCCTAGATGTGAATATCAAGCAGGACATAGCAAGGTAGGATCTCTACAGTACTTGGCACTAACAGCATTAATAAAACCAAAAAAGATAAAGCCACCTTTGCCTAGTGTTAAGAAACTAACAGAGGATAGATGGAACAAGCCCCAGAAGACCAAGGGCCACAGAGGGAGCCATACAATGAATGGACACTAGAGCTTTTAGAGGAACTTAAGAATGAAGCTGTTAGACATTTTCCTAGGATCTGGCTCCATAGCTTAGGGCAATATATCTATGAAACTTATGGGGATACTTGGGCAGGAGTGGAAGCCATAATAAGAATACTGCAACAGCTGCTGTTTATTCATTTCAGAATTGGGTGTCGACATAGCAGAATAGGCATTACTCGACAGAGGAGAGCAAGAAATGGAGCCAGTAGATCCTAGCCTAGAGCCCTGGAAGCATCCAGGAAGTCAGCCTAAGACTGCTTGTACCAATTGCTATTGTAAAAAGTGTTGCCTTCATTGCCAAGTTTGTTTCACAACAAAAGGCTTAGGCATCTCCTATGGCAGGAAGAAGCGGAGACAGCGACGAAGACCTCCTCAAGACAGTCAGACTCATCAAGTTTCTCTACCAAAGCAGTAAGTAGTGCATGTAATGCAACCTTTACAAATATTAGCAATAGTAGCATTAGTAGTAGCAGGAATAATAGCAATAATTGTGTGGTCCATAGTACTCATAGAATATAGGAAAATATTAAGACAAAGAAAAATAGATAGGTTAATTGATAAAATAAGAGAGAGAGCAGAAGACAGTGGCAATGAGAGTGAAGGGGATCAGGAAGAATTATCAGCACTTGTGGAAAGGGGGCATCTTGCTCCTTGGGACATTAATGATCTGTAGTGCTGTAGAAAAGTTGTGGGTCACAGTCTATTATGGGGTACCTGTGTGGAAAGAAACAACCACCACTCTATTTTGTGCATCAGATGCTAAAGCATATGATACAGAGGTACATAATGTTTGGGCCACACATGCCTGTGTACCCACAGACCCCAACCCACAAGAAGTAGTATTGGAAAATGTAACAGAAGATTTTAACATGTGGAAAAATAACATGGTAGAACAGATGCAGGAGGATGTAATCAATTTATGGGATCAAAGCTTAAAGCCATGTGTAAAATTAACCCCACTCTGTGTTACTTTAAATTGCAAAGATGTGAATGCTACTAATACCACTAGTAGTAGTGAGGGAATGATGGAGAGAGGAGAAATAAAAAACTGCTCTTTCAATATCACCAAAAGCATAAGAGATAAGGTGCAGAAAGAATATGCTCTTTTTTATAAACTGGATGTAGTACCAATAGATAATAAGAATAATACCAAATATAGGTTAATAAGTTGTAACACCTCAGTCATTACACAAGCCTGTCCAAAGGTATCCTTTGAACCAATTCCCATACATTATTGTGCCCCGGCTGGTTTTGCGATTCTAAAGTGTAATAATAAGACATTCAATGGAAAAGGACAATGTAAAAATGTCAGCACAGTACAATGTACACATGGAATTAGGCCAGTAGTATCAACTCAACTGCTGCTAAATGGCAGTCTAGCAGAAGAAAAGGTTGTAATTAGATCTGACAATTTTACGGACAATGCTAAAACCATAATAGTACAGCTGAATGAATCTGTAAAAATTAATTGTACAAGGCCCAGCAACAATACAAGAAAAAGTATACATATAGGACCAGGGAGAGCATTTTATACAACAGGAGAAATAATAGGAGATATAAGACAAGCACATTGTAACATTAGTAGAGCACAATGGAATAACACTTTAAAACAGATAGTTGAAAAATTAAGAGAACAATTTAATAATAAAACAATAGTCTTTACTCACTCCTCAGGAGGGGATCCAGAAATTGTAATGCACAGTTTTAATTGTGGAGGGGAATTTTTCTACTGTAATTCAACACAACTGTTTAATAGTACTTGGAATGATACTGAAAAGTCAAGTGGCACTGAAGGAAATGACACCATCATACTCCCATGCAGAATAAAACAAATTATAAACATGTGGCAGGAAGTGGGAAAAGCAATGTATGCTCCTCCCATTAAAGGACAAATTAGATGTTCATCAAATATTACAGGGCTGCTATTAACAAGAGATGGTGGTAAAAATGAGAGTGAGATCGAGATCTTCAGACCTGGAGGAGGAGACATGAGGGACAATTGGAGAAGTGAATTATATAAATATAAAGTAGTAAAAATTGAACCATTAGGAGTAGCACCCACCAAGGCAAAGAGAAGAGTGGTGCAAAGAGAAAAAAGAGCAGTGGGAATAGGAGCTTTGTTCCTTGGGTTCTTGGGAGCAGCAGGAAGCACTATGGGCGCAGCGTCAATGACACTGACGGTACAGGCCAGACAATTATTGTCTGGTATAGTGCAACAGCAAAACAATTTGCTGAGGGCTATTGAGGCGCAACAGCATATGTTGCAACTCACAGTCTGGGGCATCAAGCAGCTCCAGGCAAGAGTCCTGGCTGTGGAAAGATACCTAAAGGATCAACAGCTCATGGGGATTTGGGGTTGCTCTGGAAAACTCATTTGCACCACTGCTGTGCCTTGGAATACTAGTTGGAGTAATAAATCTCTGGATAGTATTTGGAATAACATGACCTGGATGGAGTGGGAAAAAGAAATTGAGAATTACACAAACACAATATACACCCTAATTGAAGAATCGCAGATCCAACAAGAAAAGAATGAACAAGAATTATTGGAATTAGATAAATGGGCAAGTTTGTGGAATTGGTTTGACATAACAAAATGGCTGTGGTATATAAAAATATTCATAATGATAGTAGGAGGCTTGATAGGTTTAAGAATAGTTTTTTCTGTACTTTCTATAGTGAATAGAGTTAGGCAGGGATACTCACCCTTATCGTTTCAGACCCTCCTCCCAGCAACGAGGGGACCCGACAGGCCCGAAGGAATCGAAGAAGAAGGTGGAGAGAGAGACAGAGACAGATCCGGACAATTAGTGAACGGATTCTTAGCACTTATCTGGGTCGACCTGCGGAGCCTGTTCCTCTTCAGCTACCACCGCTTGAGAGACTTACTCTTGACTGTAACGAGGATTGTGGAACTTCTGGGACGCAGGGGGTGGGAAATCCTGAAATACTGGTGGAATCTCCTACAGTATTGGAGTCAGGAACTAAAGAATAGTGCTGTTAGCTTGCTTAATGCCACAGCTATAGCAGTAGCTGAGGGGACAGATAGGATTATAGAAGTAGTACAAAGAGTTTATAGGGCTATTCTCCACATACCTACAAGAATAAGACAGGGCTTGGAAAGGGCTTTGCTATAAGATGGGTGGCAAGTGGTCAAAACATAGTGTGCCTGGATGGTCTACTGTAAGGGAAAGAATGAGACGAGCTGAGCCAGCAACAGATAGGGTGAGACAAACTGAGCCAGCAGCAGTAGGGGTGGGAGCAGTATCTCGAGACCTGGAAAAACATGGAGCAATCACAAGTAGCAATACAGCAGCTACCAATGCTGATTGTGCCTGGCTAGAAGCATATGAGGATGAGGAAGTGGGTTTTCCAGTCAGACCTCAGGTACCTTTAAGACCAATGACTTACAAGGCAGCTATAGATCTTAGCCACTTTTTAAAAGAAAAGGGGGGACTGGAAGGGCTAATTTACTCACAGAAAAGACAAGATATCCTTGATCTGTGGATCTACCACACACAAGGCTACTTCCCTGATTGGCAGAACTACACAGCAGGACCAGGGGTCAGATTTCCACTGACCTTTGGATGGTGCTTCAAGCTAGTACCAGTTGATCCAGAGAAGGTAGAAGAGGCCAATGAAGGAGAGAACAACTGCTTGTTACACCCTATGAGCCAGCATGGAATGGACGACCCAGAGAAGGAAGTGTTAGTGTGGAAGTTTGACAGCAAGCTAGCATTGCATCACGTGGCCCGAGAGCTGCATCCGGAGTACTACAAGGACTGCTGACACCGAGCTTTCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGCGTGGCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATGCTGCATATAAGCAGCTGC 5 | >NL43 6 | TGGAAGGGCTAATTTACTCCCAAAAAAGACAAGAGATCCTTGATCTGTGGATCTACCACACACAAGGCTACTTCCCTGATTGGCAGAACTACACACCAGGGCCAGGGGTCAGATATCCACTGACCTTTGGATGGTGCTTCAAGCTAGTACCAGTTGAGCCAGAGCAAGTAGAAGAGGCCAATGAAGGAGAGAACAACAGCTTGTTACACCCTATGAGCCAGCATGGGATGGATGACCCTGAGGGAGAAGTATTAGTGTGGAAGTTTGACAGCCTCCTAGCATTTCATCACATGGCCCGAGAGCTGCATCCGGAGTACTACAAAGACTGCTGACATCGAGCTTTCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGTGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATGCTACATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCTGGTTAGACCAGATCTGAGCCTGGGAGCTCTCTGGCTAACTAGGGAACCCACTGCTTAAGCCTCAATAAAGCTTGCCTTGAGTGCTCAAAGTAGTGTGTGCCCGTCTGTTGTGTGACTCTGGTAACTAGAGATCCCTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCAGTGGCGCCCGAACAGGGACTTGAAAGCGAAAGTAAAGCCAGAGGAGATCTCTCGACGCAGGACTCGGCTTGCTGAAGCGCGCACGGCAAGAGGCGAGGGGCGGCGACTGGTGAGTACGCCAAAAATTTTGACTAGCGGAGGCTAGAAGGAGAGAGATGGGTGCGAGAGCGTCGGTATTAAGCGGGGGAGAATTAGATAAATGGGAAAAAATTCGGTTAAGGCCAGGGGGAAAGAAACAATATAAACTAAAACATATAGTATGGGCAAGCAGGGAGCTAGAACGATTCGCAGTTAATCCTGGCCTTTTAGAGACATCAGAAGGCTGTAGACAAATACTGGGACAGCTACAACCATCCCTTCAGACAGGATCAGAAGAACTTAGATCATTATATAATACAATAGCAGTCCTCTATTGTGTGCATCAAAGGATAGATGTAAAAGACACCAAGGAAGCCTTAGATAAGATAGAGGAAGAGCAAAACAAAAGTAAGAAAAAGGCACAGCAAGCAGCAGCTGACACAGGAAACAACAGCCAGGTCAGCCAAAATTACCCTATAGTGCAGAACCTCCAGGGGCAAATGGTACATCAGGCCATATCACCTAGAACTTTAAATGCATGGGTAAAAGTAGTAGAAGAGAAGGCTTTCAGCCCAGAAGTAATACCCATGTTTTCAGCATTATCAGAAGGAGCCACCCCACAAGATTTAAATACCATGCTAAACACAGTGGGGGGACATCAAGCAGCCATGCAAATGTTAAAAGAGACCATCAATGAGGAAGCTGCAGAATGGGATAGATTGCATCCAGTGCATGCAGGGCCTATTGCACCAGGCCAGATGAGAGAACCAAGGGGAAGTGACATAGCAGGAACTACTAGTACCCTTCAGGAACAAATAGGATGGATGACACATAATCCACCTATCCCAGTAGGAGAAATCTATAAAAGATGGATAATCCTGGGATTAAATAAAATAGTAAGAATGTATAGCCCTACCAGCATTCTGGACATAAGACAAGGACCAAAGGAACCCTTTAGAGACTATGTAGACCGATTCTATAAAACTCTAAGAGCCGAGCAAGCTTCACAAGAGGTAAAAAATTGGATGACAGAAACCTTGTTGGTCCAAAATGCGAACCCAGATTGTAAGACTATTTTAAAAGCATTGGGACCAGGAGCGACACTAGAAGAAATGATGACAGCATGTCAGGGAGTGGGGGGACCCGGCCATAAAGCAAGAGTTTTGGCTGAAGCAATGAGCCAAGTAACAAATCCAGCTACCATAATGATACAGAAAGGCAATTTTAGGAACCAAAGAAAGACTGTTAAGTGTTTCAATTGTGGCAAAGAAGGGCACATAGCCAAAAATTGCAGGGCCCCTAGGAAAAAGGGCTGTTGGAAATGTGGAAAGGAAGGACACCAAATGAAAGATTGTACTGAGAGACAGGCTAATTTTTTAGGGAAGATCTGGCCTTCCCACAAGGGAAGGCCAGGGAATTTTCTTCAGAGCAGACCAGAGCCAACAGCCCCACCAGAAGAGAGCTTCAGGTTTGGGGAAGAGACAACAACTCCCTCTCAGAAGCAGGAGCCGATAGACAAGGAACTGTATCCTTTAGCTTCCCTCAGATCACTCTTTGGCAGCGACCCCTCGTCACAATAAAGATAGGGGGGCAATTAAAGGAAGCTCTATTAGATACAGGAGCAGATGATACAGTATTAGAAGAAATGAATTTGCCAGGAAGATGGAAACCAAAAATGATAGGGGGAATTGGAGGTTTTATCAAAGTAAGACAGTATGATCAGATACTCATAGAAATCTGCGGACATAAAGCTATAGGTACAGTATTAGTAGGACCTACACCTGTCAACATAATTGGAAGAAATCTGTTGACTCAGATTGGCTGCACTTTAAATTTTCCCATTAGTCCTATTGAGACTGTACCAGTAAAATTAAAGCCAGGAATGGATGGCCCAAAAGTTAAACAATGGCCATTGACAGAAGAAAAAATAAAAGCATTAGTAGAAATTTGTACAGAAATGGAAAAGGAAGGAAAAATTTCAAAAATTGGGCCTGAAAATCCATACAATACTCCAGTATTTGCCATAAAGAAAAAAGACAGTACTAAATGGAGAAAATTAGTAGATTTCAGAGAACTTAATAAGAGAACTCAAGATTTCTGGGAAGTTCAATTAGGAATACCACATCCTGCAGGGTTAAAACAGAAAAAATCAGTAACAGTACTGGATGTGGGCGATGCATATTTTTCAGTTCCCTTAGATAAAGACTTCAGGAAGTATACTGCATTTACCATACCTAGTATAAACAATGAGACACCAGGGATTAGATATCAGTACAATGTGCTTCCACAGGGATGGAAAGGATCACCAGCAATATTCCAGTGTAGCATGACAAAAATCTTAGAGCCTTTTAGAAAACAAAATCCAGACATAGTTATCTATCAATACATGGATGATTTGTATGTAGGATCTGACTTAGAAATAGGGCAGCATAGAACAAAAATAGAGGAACTGAGACAACATCTGTTGAGGTGGGGATTTACCACACCAGACAAAAAACATCAGAAAGAACCTCCATTCCTTTGGATGGGTTATGAACTCCATCCTGATAAATGGACAGTACAGCCTATAGTGCTGCCAGAAAAGGACAGCTGGACTGTCAATGACATACAGAAATTAGTGGGAAAATTGAATTGGGCAAGTCAGATTTATGCAGGGATTAAAGTAAGGCAATTATGTAAACTTCTTAGGGGAACCAAAGCACTAACAGAAGTAGTACCACTAACAGAAGAAGCAGAGCTAGAACTGGCAGAAAACAGGGAGATTCTAAAAGAACCGGTACATGGAGTGTATTATGACCCATCAAAAGACTTAATAGCAGAAATACAGAAGCAGGGGCAAGGCCAATGGACATATCAAATTTATCAAGAGCCATTTAAAAATCTGAAAACAGGAAAGTATGCAAGAATGAAGGGTGCCCACACTAATGATGTGAAACAATTAACAGAGGCAGTACAAAAAATAGCCACAGAAAGCATAGTAATATGGGGAAAGACTCCTAAATTTAAATTACCCATACAAAAGGAAACATGGGAAGCATGGTGGACAGAGTATTGGCAAGCCACCTGGATTCCTGAGTGGGAGTTTGTCAATACCCCTCCCTTAGTGAAGTTATGGTACCAGTTAGAGAAAGAACCCATAATAGGAGCAGAAACTTTCTATGTAGATGGGGCAGCCAATAGGGAAACTAAATTAGGAAAAGCAGGATATGTAACTGACAGAGGAAGACAAAAAGTTGTCCCCCTAACGGACACAACAAATCAGAAGACTGAGTTACAAGCAATTCATCTAGCTTTGCAGGATTCGGGATTAGAAGTAAACATAGTGACAGACTCACAATATGCATTGGGAATCATTCAAGCACAACCAGATAAGAGTGAATCAGAGTTAGTCAGTCAAATAATAGAGCAGTTAATAAAAAAGGAAAAAGTCTACCTGGCATGGGTACCAGCACACAAAGGAATTGGAGGAAATGAACAAGTAGATAAATTGGTCAGTGCTGGAATCAGGAAAGTACTATTTTTAGATGGAATAGATAAGGCCCAAGAAGAACATGAGAAATATCACAGTAATTGGAGAGCAATGGCTAGTGATTTTAACCTACCACCTGTAGTAGCAAAAGAAATAGTAGCCAGCTGTGATAAATGTCAGCTAAAAGGGGAAGCCATGCATGGACAAGTAGACTGTAGCCCAGGAATATGGCAGCTAGATTGTACACATTTAGAAGGAAAAGTTATCTTGGTAGCAGTTCATGTAGCCAGTGGATATATAGAAGCAGAAGTAATTCCAGCAGAGACAGGGCAAGAAACAGCATACTTCCTCTTAAAATTAGCAGGAAGATGGCCAGTAAAAACAGTACATACAGACAATGGCAGCAATTTCACCAGTACTACAGTTAAGGCCGCCTGTTGGTGGGCGGGGATCAAGCAGGAATTTGGCATTCCCTACAATCCCCAAAGTCAAGGAGTAATAGAATCTATGAATAAAGAATTAAAGAAAATTATAGGACAGGTAAGAGATCAGGCTGAACATCTTAAGACAGCAGTACAAATGGCAGTATTCATCCACAATTTTAAAAGAAAAGGGGGGATTGGGGGGTACAGTGCAGGGGAAAGAATAGTAGACATAATAGCAACAGACATACAAACTAAAGAATTACAAAAACAAATTACAAAAATTCAAAATTTTCGGGTTTATTACAGGGACAGCAGAGATCCAGTTTGGAAAGGACCAGCAAAGCTCCTCTGGAAAGGTGAAGGGGCAGTAGTAATACAAGATAATAGTGACATAAAAGTAGTGCCAAGAAGAAAAGCAAAGATCATCAGGGATTATGGAAAACAGATGGCAGGTGATGATTGTGTGGCAAGTAGACAGGATGAGGATTAACACATGGAAAAGATTAGTAAAACACCATATGTATATTTCAAGGAAAGCTAAGGACTGGTTTTATAGACATCACTATGAAAGTACTAATCCAAAAATAAGTTCAGAAGTACACATCCCACTAGGGGATGCTAAATTAGTAATAACAACATATTGGGGTCTGCATACAGGAGAAAGAGACTGGCATTTGGGTCAGGGAGTCTCCATAGAATGGAGGAAAAAGAGATATAGCACACAAGTAGACCCTGACCTAGCAGACCAACTAATTCATCTGCACTATTTTGATTGTTTTTCAGAATCTGCTATAAGAAATACCATATTAGGACGTATAGTTAGTCCTAGGTGTGAATATCAAGCAGGACATAACAAGGTAGGATCTCTACAGTACTTGGCACTAGCAGCATTAATAAAACCAAAACAGATAAAGCCACCTTTGCCTAGTGTTAGGAAACTGACAGAGGACAGATGGAACAAGCCCCAGAAGACCAAGGGCCACAGAGGGAGCCATACAATGAATGGACACTAGAGCTTTTAGAGGAACTTAAGAGTGAAGCTGTTAGACATTTTCCTAGGATATGGCTCCATAACTTAGGACAACATATCTATGAAACTTACGGGGATACTTGGGCAGGAGTGGAAGCCATAATAAGAATTCTGCAACAACTGCTGTTTATCCATTTCAGAATTGGGTGTCGACATAGCAGAATAGGCGTTACTCGACAGAGGAGAGCAAGAAATGGAGCCAGTAGATCCTAGACTAGAGCCCTGGAAGCATCCAGGAAGTCAGCCTAAAACTGCTTGTACCAATTGCTATTGTAAAAAGTGTTGCTTTCATTGCCAAGTTTGTTTCATGACAAAAGCCTTAGGCATCTCCTATGGCAGGAAGAAGCGGAGACAGCGACGAAGAGCTCATCAGAACAGTCAGACTCATCAAGCTTCTCTATCAAAGCAGTAAGTAGTACATGTAATGCAACCTATAATAGTAGCAATAGTAGCATTAGTAGTAGCAATAATAATAGCAATAGTTGTGTGGTCCATAGTAATCATAGAATATAGGAAAATATTAAGACAAAGAAAAATAGACAGGTTAATTGATAGACTAATAGAAAGAGCAGAAGACAGTGGCAATGAGAGTGAAGGAGAAGTATCAGCACTTGTGGAGATGGGGGTGGAAATGGGGCACCATGCTCCTTGGGATATTGATGATCTGTAGTGCTACAGAAAAATTGTGGGTCACAGTCTATTATGGGGTACCTGTGTGGAAGGAAGCAACCACCACTCTATTTTGTGCATCAGATGCTAAAGCATATGATACAGAGGTACATAATGTTTGGGCCACACATGCCTGTGTACCCACAGACCCCAACCCACAAGAAGTAGTATTGGTAAATGTGACAGAAAATTTTAACATGTGGAAAAATGACATGGTAGAACAGATGCATGAGGATATAATCAGTTTATGGGATCAAAGCCTAAAGCCATGTGTAAAATTAACCCCACTCTGTGTTAGTTTAAAGTGCACTGATTTGAAGAATGATACTAATACCAATAGTAGTAGCGGGAGAATGATAATGGAGAAAGGAGAGATAAAAAACTGCTCTTTCAATATCAGCACAAGCATAAGAGATAAGGTGCAGAAAGAATATGCATTCTTTTATAAACTTGATATAGTACCAATAGATAATACCAGCTATAGGTTGATAAGTTGTAACACCTCAGTCATTACACAGGCCTGTCCAAAGGTATCCTTTGAGCCAATTCCCATACATTATTGTGCCCCGGCTGGTTTTGCGATTCTAAAATGTAATAATAAGACGTTCAATGGAACAGGACCATGTACAAATGTCAGCACAGTACAATGTACACATGGAATCAGGCCAGTAGTATCAACTCAACTGCTGTTAAATGGCAGTCTAGCAGAAGAAGATGTAGTAATTAGATCTGCCAATTTCACAGACAATGCTAAAACCATAATAGTACAGCTGAACACATCTGTAGAAATTAATTGTACAAGACCCAACAACAATACAAGAAAAAGTATCCGTATCCAGAGGGGACCAGGGAGAGCATTTGTTACAATAGGAAAAATAGGAAATATGAGACAAGCACATTGTAACATTAGTAGAGCAAAATGGAATGCCACTTTAAAACAGATAGCTAGCAAATTAAGAGAACAATTTGGAAATAATAAAACAATAATCTTTAAGCAATCCTCAGGAGGGGACCCAGAAATTGTAACGCACAGTTTTAATTGTGGAGGGGAATTTTTCTACTGTAATTCAACACAACTGTTTAATAGTACTTGGTTTAATAGTACTTGGAGTACTGAAGGGTCAAATAACACTGAAGGAAGTGACACAATCACACTCCCATGCAGAATAAAACAATTTATAAACATGTGGCAGGAAGTAGGAAAAGCAATGTATGCCCCTCCCATCAGTGGACAAATTAGATGTTCATCAAATATTACTGGGCTGCTATTAACAAGAGATGGTGGTAATAACAACAATGGGTCCGAGATCTTCAGACCTGGAGGAGGCGATATGAGGGACAATTGGAGAAGTGAATTATATAAATATAAAGTAGTAAAAATTGAACCATTAGGAGTAGCACCCACCAAGGCAAAGAGAAGAGTGGTGCAGAGAGAAAAAAGAGCAGTGGGAATAGGAGCTTTGTTCCTTGGGTTCTTGGGAGCAGCAGGAAGCACTATGGGCGCAGCGTCAATGACGCTGACGGTACAGGCCAGACAATTATTGTCTGATATAGTGCAGCAGCAGAACAATTTGCTGAGGGCTATTGAGGCGCAACAGCATCTGTTGCAACTCACAGTCTGGGGCATCAAACAGCTCCAGGCAAGAATCCTGGCTGTGGAAAGATACCTAAAGGATCAACAGCTCCTGGGGATTTGGGGTTGCTCTGGAAAACTCATTTGCACCACTGCTGTGCCTTGGAATGCTAGTTGGAGTAATAAATCTCTGGAACAGATTTGGAATAACATGACCTGGATGGAGTGGGACAGAGAAATTAACAATTACACAAGCTTAATACACTCCTTAATTGAAGAATCGCAAAACCAGCAAGAAAAGAATGAACAAGAATTATTGGAATTAGATAAATGGGCAAGTTTGTGGAATTGGTTTAACATAACAAATTGGCTGTGGTATATAAAATTATTCATAATGATAGTAGGAGGCTTGGTAGGTTTAAGAATAGTTTTTGCTGTACTTTCTATAGTGAATAGAGTTAGGCAGGGATATTCACCATTATCGTTTCAGACCCACCTCCCAATCCCGAGGGGACCCGACAGGCCCGAAGGAATAGAAGAAGAAGGTGGAGAGAGAGACAGAGACAGATCCATTCGATTAGTGAACGGATCCTTAGCACTTATCTGGGACGATCTGCGGAGCCTGTGCCTCTTCAGCTACCACCGCTTGAGAGACTTACTCTTGATTGTAACGAGGATTGTGGAACTTCTGGGACGCAGGGGGTGGGAAGCCCTCAAATATTGGTGGAATCTCCTACAGTATTGGAGTCAGGAACTAAAGAATAGTGCTGTTAACTTGCTCAATGCCACAGCCATAGCAGTAGCTGAGGGGACAGATAGGGTTATAGAAGTATTACAAGCAGCTTATAGAGCTATTCGCCACATACCTAGAAGAATAAGACAGGGCTTGGAAAGGATTTTGCTATAAGATGGGTGGCAAGTGGTCAAAAAGTAGTGTGATTGGATGGCCTGCTGTAAGGGAAAGAATGAGACGAGCTGAGCCAGCAGCAGATGGGGTGGGAGCAGTATCTCGAGACCTAGAAAAACATGGAGCAATCACAAGTAGCAATACAGCAGCTAACAATGCTGCTTGTGCCTGGCTAGAAGCACAAGAGGAGGAAGAGGTGGGTTTTCCAGTCACACCTCAGGTACCTTTAAGACCAATGACTTACAAGGCAGCTGTAGATCTTAGCCACTTTTTAAAAGAAAAGGGGGGACTGGAAGGGCTAATTCACTCCCAAAGAAGACAAGATATCCTTGATCTGTGGATCTACCACACACAAGGCTACTTCCCTGATTGGCAGAACTACACACCAGGGCCAGGGGTCAGATATCCACTGACCTTTGGATGGTGCTACAAGCTAGTACCAGTTGAGCCAGATAAGGTAGAAGAGGCCAATAAAGGAGAGAACACCAGCTTGTTACACCCTGTGAGCCTGCATGGAATGGATGACCCTGAGAGAGAAGTGTTAGAGTGGAGGTTTGACAGCCGCCTAGCATTTCATCACGTGGCCCGAGAGCTGCATCCGGAGTACTTCAAGAACTGCTGACATCGAGCTTGCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGGCGTGGCCTGGGCGGGACTGGGGAGTGGCGAGCCCTCAGATGCTGCATATAAGCAGCTGCTTTTTGCCTGTACTGGGTCTCTCTGGTTAGACCAGATCTGAGCCTGGGAGCTCTCTGGCTAACTAGGGAACCCACTGCTTAAGCCTCAATAAAGCTTGCCTTGAGTGCTCAAAGTAGTGTGTGCCCGTCTGTTGTGTGACTCTGGTAACTAGAGATCCCTCAGACCCTTTTAGTCAGTGTGGAAAATCTCTAGCA 7 | -------------------------------------------------------------------------------- /reproduce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -e 3 | 4 | ######### Run strainline ######### 5 | 6 | srcpath=/root/capsule/code/src 7 | threads=16 8 | 9 | # small example dataset 10 | echo "Running the example dataset..." 11 | input=/root/capsule/data/example/reads.fa 12 | $srcpath/strainline.sh -i $input -o out -p pb -k 20 -t $threads 13 | 14 | 15 | ## Note: Running the following codes on 'Code Ocean' may be time-consuming because of limited computational resources ## 16 | ## For all datasets, we provide the final output haplotyps in 'result/' ## 17 | 18 | if false 19 | then 20 | # simulated data, pacbio clr, 5-HIV 21 | input=/root/capsule/data/simulated/clr/5-HIV/reads.fa.gz 22 | $srcpath/strainline.sh -i $input -o out -p pb -k 50 --maxGD 0.02 --maxLD 0.01 --maxOH 50 -t $threads 23 | 24 | ## For other different sequencing coverage data (500x,1000x,2000x,5000x,10000x) 25 | # input=/root/capsule/data/simulated/clr/5-HIV/diff_coverage/*/reads.fa.gz 26 | # $srcpath/strainline.sh -i $input -o out -p pb -k 50 --maxGD 0.01 --maxLD 0.01 --maxOH 50 -t $threads 27 | 28 | # simulated data, pacbio clr, 6-polio 29 | input=/root/capsule/data/simulated/clr/6-polio/reads.fa.gz 30 | $srcpath/strainline.sh -i $input -o out -p pb -k 100 --minIdentity 0.995 -t $threads 31 | 32 | # simulated data, pacbio clr, 10-HCV 33 | input=/root/capsule/data/simulated/clr/10-HCV/reads.fa.gz 34 | $srcpath/strainline.sh -i $input -o out -p pb -k 100 --maxGD 0.02 --maxOH 50 -t $threads 35 | 36 | # simulated data, pacbio clr, 15-ZIKV 37 | input=/root/capsule/data/simulated/clr/15-ZIKV/reads.fa.gz 38 | $srcpath/strainline.sh -i $input -o out -p pb -k 500 --minIdentity 0.995 --maxGD 0.005 --minAbun 0.01 -t $threads 39 | 40 | # simulated data, pacbio clr, 5-SARSCov2 41 | input=/root/capsule/data/simulated/clr/5-SARSCov2/reads.fa.gz 42 | $srcpath/strainline.sh -i $input -o out -p pb -k 100 --maxLD 0.01 --minAbun 0.085 --iter 3 -t $threads 43 | 44 | 45 | 46 | 47 | # simulated data, ont, 5-HIV 48 | input=/root/capsule/data/simulated/ont/5-HIV/reads.fa.gz 49 | $srcpath/strainline.sh -i $input -o out -p ont -k 20 --maxGD 0.02 --maxLD 0.01 --maxOH 50 -t $threads 50 | 51 | # simulated data, ont , 6-polio 52 | input=/root/capsule/data/simulated/ont/6-polio/reads.fa.gz 53 | $srcpath/strainline.sh -i $input -o out -p ont -k 100 --maxOH 50 --maxLD 0.01 -t $threads 54 | 55 | # simulated data, ont , 10-HCV 56 | input=/root/capsule/data/simulated/ont/10-HCV/reads.fa.gz 57 | $srcpath/strainline.sh -i $input -o out -p ont -k 100 --maxGD 0.02 --maxLD 0.01 --maxOH 50 -t $threads 58 | 59 | # simulated data, ont , 15-ZIKV 60 | input=/root/capsule/data/simulated/ont/15-ZIKV/reads.fa.gz 61 | $srcpath/strainline.sh -i $input -o out -p ont -k 100 --minIdentity 0.995 --minAbun 0.01 -t $threads 62 | 63 | # simulated data, ont , 5-SARSCov2 64 | input=/root/capsule/data/simulated/ont/5-SARSCov2/reads.fa.gz 65 | $srcpath/strainline.sh -i $input -o out -p ont -k 100 --maxLD 0.01 --minAbun 0.085 -t $threads 66 | 67 | 68 | 69 | # real data, ont, 5-PVY 70 | input=/root/capsule/data/real/5-PVY/reads.fa.gz 71 | $srcpath/strainline.sh -i $input -o out -p ont -k 100 --maxGD 0.02 --maxLD 0.01 --minOvlpLen 400 --minSeedLen 2000 --minAbun 0.03 --maxOH 20 -t $threads 72 | 73 | # real data, ont, SARSCov2 [SRA:SRP250446] 74 | input=/root/capsule/data/real/SARSCov2/SRP250446/reads.fa.gz 75 | $srcpath/strainline.sh -i $input -o out -p ont -k 50 --rmMisassembly True --minAbun 0.05 --minIdentity 0.98 --maxLD 0.01 -t $threads 76 | 77 | fi 78 | 79 | 80 | 81 | ######### Run other tools ######### 82 | 83 | ## Run canu --version, Canu snapshot (), installed on Nov4,2019 via conda ## 84 | ## use parameters recommended for metagenome assembly ## 85 | ## For PacBio CLR 86 | # canu -p out -d out genomeSize=GENOMESIZE minReadLength=500 minOverlapLength=200 corOutCoverage=10000 corMhapSensitivity=high corMinCoverage=0 redMemory=32 oeaMemory=32 batMemory=200 -pacbio-raw reads.fa 87 | ## For ONT 88 | # canu -p out -d out genomeSize=GENOMESIZE minReadLength=500 minOverlapLength=200 corOutCoverage=10000 corMhapSensitivity=high corMinCoverage=0 redMemory=32 oeaMemory=32 batMemory=200 -nanopore-raw reads.fa 89 | 90 | ## Run wtdbg2 Version: 0.0 (19830203) ## 91 | # wtdbg2 -x rs -g GENOMESIZE -t 8 -i $fq -fo out 92 | # wtpoa-cns -t 8 -i out.ctg.lay.gz -fo out.ctg.fa 93 | 94 | echo The output assemblies of simulated datasets are located here: 95 | ls /root/capsule/data/uploadedResults/simulated/*/*/haplotypes.*.fa 96 | echo 97 | echo the output assemblies of real datasets are located here: 98 | ls /root/capsule/data/uploadedResults/real/*/haplotypes.*.fa 99 | echo 100 | echo The output assemblies of simulated datasets[5-HIV, various sequencing coverages] are located here: 101 | ls /root/capsule/data/uploadedResults/simulated/*/5-HIV/*/haplotypes.*.fa 102 | echo 103 | 104 | ######### Evaluation ######### 105 | ## For assembly evaluation, we used the following commands ## 106 | # quast-5.1.0rc1/metaquast.py -r ref.fa --min-contig 500 -o out --unique-mapping -t 16 contigs.fa 107 | # cat out/runs_per_reference/ref/report.tsv 108 | -------------------------------------------------------------------------------- /src/bam2clip_fa.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Used to convert bam file into a hard/soft clipped fasta, 3 | mainly for breaking chimeric reads or removing artifact sequences 4 | in the both ends of reads. 5 | The soft clipped bases will be removed in both primary and supplementary alignments. 6 | 7 | Note: if the read is from - strand, the sequence in the bam is reversed and complementary, 8 | but the order of CIGAR string is consistent with the sequence in the bam. 9 | 10 | ''' 11 | 12 | import sys 13 | import pysam 14 | import itertools 15 | 16 | 17 | def bam2fa(bam, fa, min_read_len): 18 | out = [] 19 | with pysam.AlignmentFile(bam, 'rb') as fr: 20 | for line in fr: 21 | read_name, flag, _, _, _, cigar, _, _, _, seq = str(line).split()[:10] 22 | if flag=='4': #unmap reads 23 | continue 24 | # split cigar into list of numbers and characters all separately 25 | splitcigar = ["".join(x) for _, x in itertools.groupby(cigar, key=str.isdigit)] 26 | # print(splitcigar) 27 | if splitcigar[1] == 'S': # soft-clipped 28 | seq = seq[int(splitcigar[0]):] 29 | if splitcigar[-1] == 'S': 30 | seq = seq[:(-1 * int(splitcigar[-2]))] 31 | if len(seq) >= min_read_len: 32 | out.append('>' + read_name + '\n' + seq + '\n') 33 | 34 | with open(fa, 'w') as fw: 35 | fw.write(''.join(out)) 36 | return 37 | 38 | if __name__ == '__main__': 39 | # bam = '/Users/xiaoluo/Documents/CWI/project/vg/test.bam' 40 | # fa = 'xx.fa' 41 | # min_read_len = 300 42 | # 43 | bam, fa, min_read_len = sys.argv[1:] 44 | bam2fa(bam, fa, int(min_read_len)) 45 | -------------------------------------------------------------------------------- /src/clustering.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from multiprocessing import Pool 4 | from filter_ovlps import filter_ovlp 5 | from sort_reads import sort_reads_by_ovlps 6 | 7 | 8 | def get_read2seq(file, mode='fastq'): 9 | read2seq = {} 10 | read = '' 11 | seq = '' 12 | if mode == 'fastq': 13 | with open(file) as fr: 14 | for i, line in enumerate(fr): 15 | if i % 4 == 0: 16 | read = line.strip().strip('@') 17 | elif i % 4 == 1: 18 | seq = line.strip() 19 | read2seq[read] = seq 20 | else: 21 | continue 22 | elif mode == 'fasta': 23 | with open(file) as fr: 24 | for i, line in enumerate(fr): 25 | if i % 2 == 0: 26 | read = line.strip().strip('>') 27 | elif i % 2 == 1: 28 | seq = line.strip() 29 | read2seq[read] = seq 30 | else: 31 | continue 32 | else: 33 | print("Error: unknown mode, only fastq or fasta permitted.") 34 | sys.exit(1) 35 | return read2seq 36 | 37 | 38 | def write_fasta(k, reads, outdir, read2seq): 39 | fa = outdir + '/' + 'sread_cluster.' + str(k) + '.fa' 40 | with open(fa, 'w') as fw: 41 | for r in reads: 42 | fw.write(">" + r + "\n" + read2seq[r] + "\n") 43 | return fa 44 | 45 | 46 | def sort_reads_by_len(in_fa, outdir): 47 | id2seq = {} 48 | id = '' 49 | 50 | with open(in_fa) as fr: 51 | for line in fr: 52 | if line.startswith('>'): 53 | id = line.strip() 54 | else: 55 | seq = line.strip() 56 | id2seq[id] = [seq, len(seq)] 57 | 58 | out_fa = outdir + '/' + '.'.join(os.path.basename(in_fa).split('.')[:-1]) + '.sort_by_len.fa' 59 | fw = open(out_fa, 'w') 60 | i = 0 61 | for id, val in sorted(id2seq.items(), key=lambda d: d[1][1], reverse=True): 62 | i += 1 63 | fw.write(id + '\n' + val[0] + '\n') 64 | return out_fa 65 | 66 | 67 | def get_clusters(in_fa, outdir, topk, platform, threads, min_ovlp_len, min_identity, o, r, max_ovlps, min_sread_len,min_cluster_size): 68 | '''topk: top k seed read will be used 69 | ''' 70 | id2fa = {} # each read to its corresponding fasta 71 | id = 0 72 | info = '' 73 | with open(in_fa) as fr: 74 | for line in fr: 75 | if line.startswith('>'): 76 | info += line 77 | else: 78 | info += line 79 | id2fa[id] = info 80 | info = '' 81 | id += 1 82 | 83 | read2seq = get_read2seq(in_fa, 'fasta') 84 | k = 0 85 | used_reads = {} 86 | cluster_list = [] 87 | for id in range(len(id2fa)): 88 | if k >= topk: 89 | break 90 | print('processing the {} seed read...'.format(k + 1)) 91 | 92 | header, seq = id2fa[id].strip().split('\n') 93 | if len(seq) < min_sread_len: 94 | break 95 | sread = header[1:] # seed read 96 | if sread in used_reads: 97 | continue 98 | with open(outdir + '/sread.fa', 'w') as fw: 99 | fw.write(header + '\n' + seq + '\n') 100 | # compute overlaps for seed read 101 | paf = outdir + '/sread.paf' 102 | filtered_paf = outdir + '/sread.filter.paf' 103 | 104 | # version 1 105 | os.system("minimap2 -cx ava-{} -t {} {} {}|cut -f 1-12 >{}".format(platform, threads, outdir + '/sread.fa', in_fa, paf)) 106 | 107 | # version 2: do not use preset 108 | # os.system("minimap2 -c -k15 -w11 -t {} {} {}|cut -f 1-12 >{}".format(threads, outdir + '/sread.fa', in_fa, paf)) 109 | # os.system("minimap2 -c -Hk23 -Xw7 -m100 -g10000 --max-chain-skip 25 -t {} {} {}|cut -f 1-12 >{}".format(threads, outdir + '/sread.fa', in_fa, paf)) 110 | # os.system("minimap2 -cx asm5 -t {} {} {}|cut -f 1-12 >{}".format(threads, outdir + '/sread.fa', in_fa, paf)) # intra-species asm-to-asm alignment 111 | # os.system("minimap2 -X -c -k 21 -w 11 -s 60 -m 90 -n 2 -r 0 -A 4 -B 2 --end-bonus=100 -t {} {} {}|cut -f 1-12 >{}".format(threads, outdir + '/sread.fa', in_fa, paf)) # use parameters for short reads, from OGRE 112 | 113 | # if os.path.getsize(paf) <= 0: 114 | # continue 115 | filter_ovlp(paf, filtered_paf, min_ovlp_len, min_identity, o, r, max_ovlps, rm_extra_ovlps=True) 116 | print('read overlaps filtering finished.') 117 | 118 | reads = [] # read IDs in this cluster 119 | 120 | if os.path.getsize(filtered_paf) == 0: 121 | reads.append(sread) 122 | else: 123 | with open(filtered_paf, 'r') as fr: 124 | for line in fr: 125 | a = line.split() 126 | reads.extend([a[0], a[5]]) 127 | reads = set(reads) 128 | if len(reads) < min_cluster_size: # iter2,only one contig is also fine 129 | # if len(reads)<5: 130 | continue 131 | sread_cluster_fa = write_fasta(k, reads, outdir, read2seq) 132 | for read in reads: 133 | used_reads[read] = 1 134 | 135 | # sort reads 136 | sorted_fa = sort_reads_by_ovlps(sread_cluster_fa, filtered_paf, outdir, by_length=1) 137 | cluster_list.append(sorted_fa) 138 | k += 1 139 | return cluster_list 140 | 141 | 142 | def get_consensus(param): 143 | # compute consensus 144 | i, cluster_fa, outdir = param 145 | 146 | #sort reads before run spoa, already sorted in get_clusters() 147 | consensus = os.popen('spoa -l 0 {}'.format(cluster_fa)).read() 148 | # consensus = os.popen('abpoa -m 1 {}'.format(cluster_fa)).read() #less computational cost,but result seems not good as spoa 149 | 150 | consensus = consensus.strip().split('\n')[-1] 151 | header = os.popen('cat {}|head -1'.format(cluster_fa)).read() 152 | out_fa = outdir + '/contig.' + str(i) + '.fa' 153 | with open(out_fa, 'w') as fw: 154 | fw.write(header + consensus + '\n') 155 | return out_fa 156 | 157 | 158 | def get_consensus_parallel(cluster_list, threads, outdir): 159 | pool = Pool(threads) 160 | params = [(i, cluster, outdir) for i, cluster in enumerate(cluster_list)] 161 | pool.map(get_consensus, params, chunksize=1) # ordered 162 | pool.close() 163 | pool.join() 164 | # with open(outdir + '/contigs.fa', 'w'd) as fw: 165 | # fw.write(''.join(out)) 166 | return 167 | 168 | 169 | if __name__ == '__main__': 170 | # topk: top k seed reads 171 | in_fa, outdir, topk, platform, threads, min_ovlp_len, min_identity, o, r, max_ovlps, min_sread_len = sys.argv[1:] 172 | cluster_list = get_clusters(in_fa, outdir, int(topk), platform, int(threads), int(min_ovlp_len), 173 | float(min_identity), int(o), float(r), int(max_ovlps), int(min_sread_len),min_cluster_size=1) 174 | get_consensus_parallel(cluster_list, int(threads), outdir) 175 | os.system('rm -rf {}/sread*'.format(outdir)) 176 | -------------------------------------------------------------------------------- /src/compute_ANI.py: -------------------------------------------------------------------------------- 1 | 2 | import sys 3 | import os 4 | 5 | def compute_ANI_ij(fa,i,j,outdir): 6 | ''' 7 | compute the ANI of the i <-> j th sequence in the fasta (one sequence per line) 8 | ''' 9 | try: 10 | i_fa="{}/seq.{}.fa".format(outdir,i) 11 | j_fa="{}/seq.{}.fa".format(outdir,j) 12 | os.system("head -{} {}|tail -2 >{}".format(i*2,fa,i_fa)) 13 | 14 | os.system("head -{} {}|tail -2 >{}".format(j*2,fa,j_fa)) 15 | os.system("fastANI -q {} -r {} -o {}/ani.{}.{}.out -t 1 >/dev/null 2>&1".format(i_fa,j_fa,outdir,i,j)) 16 | os.system("rm -f {} {}".format(i_fa,j_fa)) 17 | except: 18 | raise Exception("Failed to extract sequences for ANI computation.") 19 | 20 | 21 | if __name__ == '__main__': 22 | fa,i,j,outdir=sys.argv[1:] 23 | i =int(i) 24 | j =int(j) 25 | os.system("mkdir -p {}".format(outdir)) 26 | compute_ANI_ij(fa,i,j,outdir) 27 | -------------------------------------------------------------------------------- /src/est_abundance.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | ############################################## 6 | ### Step4: low frequent haplotypes removal ### 7 | ############################################## 8 | old_hap_fa=$1 9 | corrected_reads_fa=$2 #corrected read fasta 10 | min_abun=$3 #0.02 11 | 12 | percent_identity=97 13 | threads=36 14 | export min_abundance=$min_abun 15 | 16 | mkdir -p filter_by_abun2 17 | cd filter_by_abun2 || exit 18 | #fa_read=../corrected.0.fa #corrected reads 19 | 20 | for i in {1..2}; 21 | do 22 | if [[ $i == 1 ]];then 23 | cat $old_hap_fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 24 | else 25 | cat haplotypes.final.fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 26 | fi 27 | minimap2 -a --secondary=no -t $threads haps.fa $corrected_reads_fa | samtools view -F 3584 -b -t $threads | samtools sort - >haps.bam 28 | 29 | jgi_summarize_bam_contig_depths haps.bam --percentIdentity $percent_identity --outputDepth haps.depth 30 | 31 | perl -e 'open A,"haps.depth";;my$alldepth=0;while(){my@a=split;$alldepth+=$a[2];}close A; \ 32 | open A,"haps.depth";;while(){my@a=split;my$d=$a[2]/$alldepth;print "$a[0]\t$a[1]\t$a[2]\t$d\n";}close A; ' | 33 | sort -k4nr >haps.depth.sort 34 | 35 | perl -e ' my%id2seq;$/=">";open A,"haps.fa";;while(){chomp;my@a=split;$id2seq{$a[0]}=$a[1];}close A; 36 | $/="\n"; open A,"haps.depth.sort";my$k=0;while(){chomp;my@a=split;next if $a[-1]<$ENV{"min_abundance"}; 37 | my$abun=sprintf "%.3f",$a[-1];my$cov=sprintf "%.0f",$a[-2]; $k+=1;print ">hap$k $cov"."x freq=$abun\n$id2seq{$a[0]}\n";}close A; ' >haplotypes.final.fa 38 | done 39 | 40 | cp haplotypes.final.fa ../victor.fa 41 | 42 | echo 'All steps finished successfully.' 43 | -------------------------------------------------------------------------------- /src/filter_ovlps.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | 4 | __author__ = "Xiao Luo" 5 | 6 | usage = ''' 7 | This program is used to filter long read overlaps(i.e.,duplicate or internal overlaps) 8 | which are generated by minimap2 9 | ''' 10 | 11 | 12 | def paf_to_str(paf, min_len, min_identity, cigar=False): 13 | ''' 14 | read paf file into a string, only keep 12 columns to reduce memory 15 | ''' 16 | if os.path.getsize(paf) == 0: 17 | return '' 18 | ovlp_str = '' 19 | with open(paf, 'r') as fr: 20 | for line in fr: 21 | a = line.split() 22 | if int(a[10]) >= min_len and float(a[9]) / float(a[10]) >= min_identity: 23 | if cigar: 24 | ovlp_str += line # keep all infor, like CIGAR 25 | else: 26 | ovlp_str += '\t'.join(line.split()[:12]) + '\n' 27 | return ovlp_str.strip() 28 | 29 | 30 | def rm_dupovlp(ovlp_str): 31 | ''' 32 | only keep the longest overlap if multiple overlaps exist for one pair reads 33 | and remove self-overlaps 34 | ''' 35 | ovlp = {} 36 | self_counts = 0 37 | dup_counts = 0 38 | for line in ovlp_str.strip().split('\n'): 39 | a = line.strip().split() 40 | qname = a[0] 41 | tname = a[5] 42 | if qname == tname: 43 | self_counts += 1 44 | continue 45 | ovlp_len = int(a[10]) 46 | key = ':'.join(sorted([qname, tname])) 47 | if key not in ovlp: 48 | ovlp[key] = line 49 | elif ovlp_len > int(ovlp[key].split()[10]): 50 | ovlp[key] = line 51 | dup_counts += 1 52 | 53 | ovlp_str2 = '\n'.join([ovlp[key] for key in ovlp.keys()]) 54 | print("self-overlap counts:" + str(self_counts)) 55 | print("duplicated overlap counts:" + str(dup_counts)) 56 | ovlp = {} 57 | return ovlp_str2 58 | 59 | 60 | def keep_max_ovlps(ovlp_str, max_ovlps, rm_extra_ovlps=False): 61 | ''' 62 | only keep the top max_ovlps(with decrease order of overlap length) overlaps for each read, 63 | in the final result there may be some reads that have more than max_ovlps in all, which is reasonable 64 | rm_extra_ovlps: is equal to delete edges which are out of max number overlaps in the overlap graph 65 | ''' 66 | out = [] 67 | rr2line = {} 68 | used_rr = {} 69 | if not rm_extra_ovlps: 70 | for line in ovlp_str.strip().split('\n'): 71 | a = line.strip().split() 72 | qname = a[0] 73 | tname = a[5] 74 | rr2line.setdefault(qname, {})[tname] = line 75 | rr2line.setdefault(tname, {})[qname] = line 76 | used_rr.setdefault(qname, {})[tname] = 0 77 | used_rr.setdefault(tname, {})[qname] = 0 78 | reads = list(rr2line.keys()) 79 | for read in reads: 80 | if read not in rr2line: continue 81 | if len(rr2line[read]) <= max_ovlps: 82 | ovlp_reads = list(rr2line[read].keys()) 83 | else: 84 | all_ovlp_reads = [t[0] for t in 85 | sorted(rr2line[read].items(), key=lambda d: int(d[1].split()[10]), reverse=True)] 86 | ovlp_reads = all_ovlp_reads[:max_ovlps] 87 | 88 | for ovlp_read in ovlp_reads: 89 | if not used_rr[read][ovlp_read]: 90 | out.append(rr2line[read][ovlp_read]) 91 | used_rr[read][ovlp_read] = 1 92 | used_rr[ovlp_read][read] = 1 93 | # del rr2line[ovlp_read][read] 94 | # del rr2line[read] 95 | else: # TODO still a bug? 96 | for line in ovlp_str.strip().split('\n'): 97 | a = line.strip().split() 98 | qname = a[0] 99 | tname = a[5] 100 | rr2line.setdefault(qname, {})[tname] = line 101 | rr2line.setdefault(tname, {})[qname] = line 102 | reads = list(rr2line.keys()) 103 | for read in reads: 104 | if read not in rr2line: continue 105 | if len(rr2line[read]) <= max_ovlps: 106 | ovlp_reads = list(rr2line[read].keys()) 107 | else: 108 | all_ovlp_reads = [t[0] for t in 109 | sorted(rr2line[read].items(), key=lambda d: int(d[1].split()[10]), reverse=True)] 110 | ovlp_reads = all_ovlp_reads[:max_ovlps] 111 | for r in all_ovlp_reads[max_ovlps:]: 112 | if r in rr2line: 113 | del rr2line[r][read] 114 | # print('{} overlapped reads number:{}'.format(read,len(ovlp_reads))) 115 | 116 | for ovlp_read in ovlp_reads: 117 | out.append(rr2line[read][ovlp_read]) 118 | del rr2line[ovlp_read][read] 119 | del rr2line[read] 120 | 121 | ovlp_str2 = '\n'.join(out) 122 | 123 | # delet large variables 124 | del rr2line, used_rr, out 125 | 126 | return ovlp_str2 127 | 128 | 129 | def rm_intermatch(ovlp_str, o=1000, r=0.8): 130 | ''' 131 | filter internal match overlaps 132 | o: overhang length 133 | r: ratio 134 | ''' 135 | ovlps = [] 136 | inter = 0 137 | for line in ovlp_str.strip().split('\n'): 138 | a = line.strip().split() 139 | l1 = int(a[1]) 140 | b1 = int(a[2]) 141 | e1 = int(a[3]) 142 | direction = a[4] 143 | l2 = int(a[6]) 144 | if direction == '-': 145 | ''' 146 | - overhang region: *** 147 | - b2,e2: both are the cordinates in the following sequence, 148 | no matter what the direction is. 149 | 150 | b1 e1 l1 151 | v -------------------------> 152 | |***|/////////|*****| 153 | w <---------------------------- 154 | b2 e2 l2 155 | ''' 156 | b2 = l2 - int(a[8]) # end in original seq 157 | e2 = l2 - int(a[7]) # start in original seq 158 | else: 159 | b2 = int(a[7]) 160 | e2 = int(a[8]) 161 | 162 | # from algorithm 5 in minimap paper 163 | overhang = min(b1, b2) + min(l1 - e1, l2 - e2) 164 | maplen = max(e1 - b1, e2 - b2) 165 | 166 | if overhang > min(o, maplen * r): 167 | # internal match 168 | inter += 1 169 | continue 170 | else: 171 | ovlps.append(line) 172 | ovlp_str2 = '\n'.join(ovlps) 173 | print("internal overlap counts:" + str(inter)) 174 | del ovlps 175 | return ovlp_str2 176 | 177 | 178 | def ovlp_str2paf(ovlp_str, paf): 179 | with open(paf, 'w') as fw: 180 | fw.write(ovlp_str) 181 | 182 | def rm_extra_ovlps(in_paf,out_paf,max_ovlps, rm_extra_ovlps): 183 | if os.path.getsize(in_paf)==0: 184 | open(out_paf,'w').close() 185 | return out_paf 186 | ovlp_str = paf_to_str(in_paf, 0, 0) 187 | print('keeping the max number of overlaps...') 188 | ovlp_str = keep_max_ovlps(ovlp_str, max_ovlps, rm_extra_ovlps) 189 | ovlp_str2paf(ovlp_str, out_paf) 190 | return out_paf 191 | 192 | def filter_ovlp(in_paf, out_paf, min_ovlp_len=1000, min_identity=0.9, o=800, r=0.8, max_ovlps=None, 193 | rm_extra_ovlps=False, cigar=False): 194 | ovlp_str = paf_to_str(in_paf, min_ovlp_len, min_identity, cigar) # filter wrong overlaps 195 | 196 | if ovlp_str: 197 | print('removing internal overlaps...') 198 | ovlp_str = rm_intermatch(ovlp_str, o, r) 199 | if ovlp_str: 200 | print('removing duplicated overlaps...') 201 | ovlp_str = rm_dupovlp(ovlp_str) 202 | 203 | if max_ovlps and ovlp_str: 204 | print('keeping the max number of overlaps...') 205 | ovlp_str = keep_max_ovlps(ovlp_str, max_ovlps, rm_extra_ovlps) 206 | 207 | if not ovlp_str: 208 | print('No overlap any more. Stop iterating') 209 | 210 | print('writing the filtered overlaps into paf file...') 211 | ovlp_str2paf(ovlp_str, out_paf) 212 | return 213 | 214 | 215 | def main(): 216 | # in_paf = "../data/local/xx.paf" # ovlpf or paf file 217 | # out_paf="../data/local/xx.filtered.paf" 218 | in_paf, out_paf, min_ovlp_len, min_identity, o = sys.argv[1:] 219 | filter_ovlp(in_paf, out_paf, int(min_ovlp_len), float(min_identity), int(o)) 220 | return 221 | 222 | 223 | if __name__ == '__main__': 224 | sys.exit(main()) 225 | -------------------------------------------------------------------------------- /src/genome_divergence.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | 4 | '''This program is used to compute the divergence between two genomes from two fasta files''' 5 | 6 | 7 | def fasta_len(fa): 8 | sum_len = 0 9 | with open(fa, 'r') as fr: 10 | for line in fr: 11 | if line.startswith('>'): 12 | continue 13 | else: 14 | sum_len += len(line.strip()) 15 | return sum_len 16 | 17 | 18 | def cal_genome_divergence(fa1, fa2): 19 | paf_out = os.popen("minimap2 -cx asm20 -t 16 {} {} 2>/dev/null".format(fa1, fa2)).read().strip().split('\n') 20 | divergence = 1.0 21 | if not paf_out[0]: 22 | return divergence,divergence 23 | matched_len = 0 # identical bases 24 | ovlp_len = 0 # including mismatches and gaps 25 | for i, line in enumerate(paf_out): 26 | a = line.split('\t') 27 | matched_len += int(a[9]) 28 | ovlp_len += int(a[10]) 29 | fa1_len = fasta_len(fa1) 30 | fa2_len = fasta_len(fa2) 31 | 32 | fa1_uniq_ovlplen = os.popen("minimap2 -ax asm20 -t 16 {} {} 2>/dev/null|samtools sort - |samtools depth -|wc -l". 33 | format(fa1, fa2)).read().strip() 34 | fa2_uniq_ovlplen = os.popen("minimap2 -ax asm20 -t 16 {} {} 2>/dev/null|samtools sort - |samtools depth -|wc -l". 35 | format(fa2, fa1)).read().strip() 36 | fa1_uniq_ovlplen = int(fa1_uniq_ovlplen) 37 | fa2_uniq_ovlplen = int(fa2_uniq_ovlplen) 38 | global_divergence = round(1 - matched_len / (ovlp_len + fa1_len - fa1_uniq_ovlplen + fa2_len - fa2_uniq_ovlplen), 4) 39 | 40 | ovlp_divergence = round(1 - matched_len / ovlp_len, 4) # only consider the overlap regions 41 | 42 | return global_divergence, ovlp_divergence 43 | 44 | 45 | if __name__ == '__main__': 46 | fa1, fa2 = sys.argv[1:] 47 | # fa1='../data/1.fa' 48 | # fa2='../data/2.fa' 49 | global_divergence, ovlp_divergence = cal_genome_divergence(fa1, fa2) 50 | print('\n'+'-'*50) 51 | print('The global genome divergence is : {}%'.format(global_divergence * 100)) 52 | print('The overlap regions divergence is: {}%'.format(ovlp_divergence * 100)) 53 | print('-'*50) 54 | 55 | -------------------------------------------------------------------------------- /src/reformat_fa.py: -------------------------------------------------------------------------------- 1 | import sys 2 | 3 | 4 | def reformat_fasta(in_fa, out_fa): 5 | id2seq = {} 6 | id = '' 7 | seq = '' 8 | with open(in_fa) as fr: 9 | for line in fr: 10 | if line.startswith('>'): 11 | if id: 12 | id2seq[id] = [seq, len(seq)] 13 | seq = '' 14 | id = line.strip() 15 | else: 16 | seq += line.strip() 17 | id2seq[id] = [seq, len(seq)] 18 | 19 | fw = open(out_fa, 'w') 20 | i = 0 21 | for id, val in sorted(id2seq.items(), key=lambda d: d[1][1], reverse=True): 22 | i += 1 23 | fw.write('>r' + str(i) + '\n' + val[0] + '\n') 24 | return out_fa 25 | 26 | 27 | if __name__ == '__main__': 28 | in_fa, out_fa = sys.argv[1:] 29 | reformat_fasta(in_fa, out_fa) 30 | -------------------------------------------------------------------------------- /src/rm_misassembly.clip.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import itertools 4 | from multiprocessing import Pool 5 | 6 | def get_posi_by_depth(lst, min_cov, w=5): 7 | ''' 8 | get the start and end positions of fragments if there are low coverage regions 9 | in the middle of sequence, the input should be better trimmed at both ends. 10 | ''' 11 | min_cov = min_cov # * 0.8 # becuase of unstable coverage 12 | min_len = 500 # TODO, min length for output sequence 13 | k = int(len(lst) / w) + 1 14 | mean_cov = 0 15 | start = -1 16 | end = -1 17 | flag = False 18 | posi_list = [] 19 | for i in range(k): 20 | mean_cov = sum([int(posi_cov.strip().split()[1]) for posi_cov in lst[i * w:(i + 1) * w]]) / w 21 | # print('mean_cov:{}'.format(mean_cov)) 22 | if mean_cov < min_cov: 23 | if start != -1: 24 | if (end - start) >= min_len: 25 | posi_list.append((start, end, end - start)) 26 | start = -1 27 | end = -1 28 | flag = False 29 | continue 30 | elif mean_cov >= min_cov and not flag: 31 | start = int(lst[i * w].strip().split()[0]) 32 | end = int(lst[min(len(lst), (i + 1) * w) - 1].strip().split()[0]) 33 | flag = True 34 | elif flag: 35 | 36 | end = int(lst[min(len(lst), (i + 1) * w) - 1].strip().split()[0]) 37 | # print('end:{}'.format(end)) 38 | if flag and ((end - start) >= min_len): 39 | posi_list.append((start, end, end - start)) # add the last one 40 | return posi_list 41 | 42 | 43 | def cov_based_misasm_removal_xx(param): 44 | i, reads_fa,outdir, min_cov, threads, trim_ends = param 45 | contig_fa='contig.{}.fa'.format(i) 46 | os.system("mkdir -p {}".format(outdir)) 47 | out_fa = outdir + '/xx.{}.fa'.format(i) 48 | 49 | # compute coverage for each base, filter secondary and supplementary alignments at first 50 | bam = outdir + "/" + str(i) + ".bam" 51 | os.system("minimap2 -a" + " --secondary=no -t 1 " +" "+ contig_fa + " " + reads_fa + \ 52 | " 2>/dev/null |samtools view -hS -F 2048 -|samtools sort -@ 1 - >" + bam) 53 | # print("minimap2 -a" + " --secondary=no -t " + str(threads) + contig_fa + " " + reads_fa + \ 54 | # " 2>/dev/null |samtools view -hS -F 2048 -|samtools sort -@ 24 - >" + bam) 55 | 56 | # TODO need to consider the low coverage region in the middle of sequence 57 | # @@ check why many errors in the middle of some super reads: 58 | # some reads are assigned into a wrong haplotype group or reads in the middle are belong to 59 | # one haplotype and reads in both ends are belong to the other, which may cause this mistake, therefore, 60 | # one should split the super reads into different parts and only keep the short fragments 61 | # (OR only keep the longest fragment) that are satisfied with min coverage requirement. 62 | """ 63 | positions = os.popen("samtools depth " + bam + "|awk '$3>=" + str(min_cov) + \ 64 | "'|cut -f 2|sed -n '1p;$p'").read().strip() 65 | if positions: 66 | [start, end] = positions.split("\n") # 1 based for sam 67 | else: 68 | print("{}.{}: no sequence satisfies the requirement of min coverage".format(i, hap)) 69 | open(out_fa, 'w').close() # new an empty file 70 | return 71 | """ 72 | 73 | if os.path.getsize(bam) == 0: 74 | print("{}: no read can be aligned to the super read, skipping...".format(i), 75 | file=open("{}/log".format(outdir), 'a')) 76 | open(out_fa, 'w').close() # new an empty file 77 | return 78 | else: 79 | positions = os.popen("samtools depth " + bam + "|awk '{print NR, $0}'" + "|awk '$4>=" + str(min_cov) + \ 80 | "'|cut -f 1 -d ' '|sed -n '1p;$p'").read().strip() 81 | if positions: 82 | [a, b] = [int(x) for x in positions.split("\n")] # line number 83 | posi_cov_list = os.popen("samtools depth " + bam + "|cut -f 2,3").read().strip().split('\n') 84 | posi_list = get_posi_by_depth(posi_cov_list[(a - 1):b], min_cov, w=5) # 1 based for sam 85 | print('posi_list:{}'.format(posi_list)) 86 | else: 87 | print("contig.{}.fa: no sequence satisfies the requirement of min coverage".format(i), 88 | file=open("{}/log".format(outdir), 'a')) 89 | open(out_fa, 'w').close() # new an empty file 90 | return 91 | 92 | with open(contig_fa, "r") as fr: 93 | seq = fr.readline() 94 | seq = fr.readline().strip() 95 | 96 | sub_k = 1 97 | if trim_ends: 98 | for start, end, _ in posi_list: 99 | consensus = seq[(int(start) - 1):int(end)] 100 | if len(posi_list) == 1: 101 | head = ">c_{}\n".format(i) 102 | else: 103 | head = ">c_{}_sub{}\n".format(i, sub_k) 104 | with open(out_fa, 'a') as fw: 105 | fw.write(head + consensus + '\n') 106 | sub_k += 1 107 | else: 108 | # trim ends with low coverage and also break at the misassembled positions in the middle regions 109 | for start, end, _ in posi_list: 110 | if len(posi_list) == 1: 111 | consensus = seq[(int(start) - 1):int(end)] 112 | head = ">contig_{}\n".format(i) 113 | else: 114 | if sub_k == 1: 115 | consensus = seq[:int(end)] 116 | elif sub_k == len(posi_list): 117 | consensus = seq[(int(start) - 1):] 118 | else: 119 | consensus = seq[(int(start) - 1):int(end)] 120 | head = ">contig_{}_sub{}\n".format(i, sub_k) 121 | with open(outdir+'/contig.{}.sub{}.fa'.format(i,sub_k), 'w') as fw: 122 | fw.write(head + consensus + '\n') 123 | sub_k += 1 124 | 125 | return 126 | 127 | 128 | def cov_based_misasm_removal(param): 129 | i, reads_fa, all_bam, outdir, min_cov, threads, trim_ends = param 130 | # os.system("mkdir -p {}".format(outdir)) 131 | out_fa = outdir + '/xx.{}.fa'.format(i) 132 | 133 | 134 | # compute coverage for each base, filter secondary and supplementary alignments at first 135 | bam = outdir + "/contig" + str(i) + ".bam" 136 | os.system("samtools view -b {} contig{} >{}".format(all_bam,str(i),bam)) 137 | os.system("samtools index {}".format(bam)) 138 | 139 | if os.path.getsize(bam) == 0: 140 | print("contig{}: bam is empty, skipping...".format(i), 141 | file=open("{}/log".format(outdir), 'a')) 142 | open(out_fa, 'w').close() # new an empty file 143 | return 144 | else: 145 | positions = os.popen("samtools depth " + bam + "|awk '{print NR, $0}'" + "|awk '$4>=" + str(min_cov) + \ 146 | "'|cut -f 1 -d ' '|sed -n '1p;$p'").read().strip() 147 | if positions: 148 | [a, b] = [int(x) for x in positions.split("\n")] # line number 149 | posi_cov_list = os.popen("samtools depth " + bam + "|cut -f 2,3").read().strip().split('\n') 150 | posi_list = get_posi_by_depth(posi_cov_list[(a - 1):b], min_cov, w=5) # 1 based for sam 151 | print('# contig:{}, posi_list:{}'.format(i,posi_list)) 152 | else: 153 | print("contig.{}.fa: no sequence satisfies the requirement of min coverage".format(i), 154 | file=open("{}/log".format(outdir), 'a')) 155 | open(out_fa, 'w').close() # new an empty file 156 | return 157 | 158 | contig="contig{}".format(i) 159 | seq = contig2seq[contig] 160 | 161 | sub_k = 1 162 | if trim_ends: 163 | for start, end, _ in posi_list: 164 | consensus = seq[(int(start) - 1):int(end)] 165 | if len(posi_list) == 1: 166 | head = ">c_{}\n".format(i) 167 | else: 168 | head = ">c_{}_sub{}\n".format(i, sub_k) 169 | with open(out_fa, 'a') as fw: 170 | fw.write(head + consensus + '\n') 171 | sub_k += 1 172 | else: 173 | # trim ends with low coverage and also break at the misassembled positions in the middle regions 174 | for start, end, _ in posi_list: 175 | if len(posi_list) == 1: 176 | consensus = seq[(int(start) - 1):int(end)] 177 | head = ">contig_{}\n".format(i) 178 | else: 179 | if sub_k == 1: 180 | consensus = seq[:int(end)] 181 | elif sub_k == len(posi_list): 182 | consensus = seq[(int(start) - 1):] 183 | else: 184 | consensus = seq[(int(start) - 1):int(end)] 185 | head = ">contig_{}_sub{}\n".format(i, sub_k) 186 | with open(outdir + '/contig.{}.sub{}.fa'.format(i, sub_k), 'w') as fw: 187 | fw.write(head + consensus + '\n') 188 | sub_k += 1 189 | 190 | return 191 | 192 | 193 | def clip_based_misasm_removal(read_fa, hap_fa, threads, outdir, min_clip_count): 194 | '''remove misassemblies based on clipped read alignments''' 195 | os.system("mkdir -p {}".format(outdir)) 196 | sam = outdir + '/tmp.sam' 197 | min_clip_len = 5 198 | 199 | os.system( 200 | "minimap2 -a --secondary=no {} {} -t {}|samtools view -F 2048 - >{}".format(hap_fa, read_fa, threads, sam)) 201 | hap2bpposi_tmp = {} 202 | hap2bpposi = {} 203 | 204 | with open(sam, 'r') as fr: 205 | for line in fr: 206 | a = line.split() 207 | hap, posi, cigar, seq = a[2], int(a[3]), a[5], a[9] 208 | read_len = len(seq) 209 | bp_posi = -1 # breakpoint position at target sequence 210 | # split cigar into list of numbers and characters all separately 211 | splitcigar = ["".join(x) for _, x in itertools.groupby(cigar, key=str.isdigit)] # 25S100M2S 212 | if splitcigar[1] == 'S': # soft-clipped, left 213 | if int(splitcigar[0]) < min_clip_len: 214 | if splitcigar[-1] == 'S' and int(splitcigar[-2]) >= min_clip_len: 215 | bp_posi = posi + read_len - int(splitcigar[-2]) # soft-clipped on both sides,such as 2S1000M50S 216 | else: 217 | continue 218 | else: 219 | bp_posi = posi 220 | elif splitcigar[-1] == 'S': # soft-clipped, right 221 | if int(splitcigar[-2]) < min_clip_len: 222 | continue 223 | else: 224 | bp_posi = posi + read_len - int(splitcigar[-2]) 225 | else: 226 | continue 227 | if hap in hap2bpposi_tmp: 228 | hap2bpposi_tmp[hap].append(bp_posi) 229 | else: 230 | hap2bpposi_tmp[hap] = [bp_posi] 231 | 232 | for hap, posi_list in hap2bpposi_tmp.items(): 233 | posi2count = {} 234 | for posi in posi_list: 235 | if posi in posi2count: 236 | posi2count[posi] += 1 237 | else: 238 | posi2count[posi] = 1 239 | for posi, count in posi2count.items(): 240 | if count >= min_clip_count: 241 | print("## posi, count:{},{}".format(posi,count)) 242 | if hap in hap2bpposi: 243 | hap2bpposi[hap].append(posi) 244 | else: 245 | hap2bpposi[hap] = [posi] 246 | 247 | hap2bpposi = {hap: sorted(posi_list) for hap, posi_list in hap2bpposi.items()} 248 | print('## haplotype to breakpoint position: {}'.format(hap2bpposi)) 249 | hap='' 250 | out_info=[] 251 | i=0 252 | with open(hap_fa) as fr: 253 | for line in fr: 254 | sub_seqs=[] 255 | if line.startswith('>'): 256 | hap=line.strip()[1:] 257 | else: 258 | seq=line.strip() 259 | start=0 260 | if hap not in hap2bpposi: 261 | i+=1 262 | out_info.append('>hap_'+str(i)+'\n'+seq+'\n') 263 | continue 264 | for bp_posi in hap2bpposi[hap]: 265 | sub_seq = seq[start:(bp_posi-1)] 266 | sub_seqs.append(sub_seq) 267 | start=bp_posi-1 268 | 269 | sub_seqs.append(seq[start:]) #last sub sequence 270 | for s in sub_seqs: 271 | if len(s)<500: #filter short sequences 272 | continue 273 | i+=1 274 | out_info.append('>hap_' + str(i)+'\n'+s+'\n') 275 | 276 | with open(outdir+'/haplotypes.final.fasta','w') as fw: 277 | fw.write(''.join(out_info)) 278 | return 279 | 280 | 281 | 282 | 283 | if __name__ == '__main__': 284 | read_fa, contig_fa, outdir, threads= sys.argv[1:] 285 | os.system("mkdir -p {}".format(outdir)) 286 | clip_based_misasm_removal(read_fa,contig_fa , threads, outdir, min_clip_count=10) 287 | -------------------------------------------------------------------------------- /src/rm_misassembly.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import itertools 4 | from multiprocessing import Pool 5 | 6 | def get_posi_by_depth(lst, min_cov, w=5): 7 | ''' 8 | get the start and end positions of fragments if there are low coverage regions 9 | in the middle of sequence, the input should be better trimmed at both ends. 10 | ''' 11 | min_cov = min_cov # * 0.8 # becuase of unstable coverage 12 | min_len = 500 # TODO, min length for output sequence 13 | k = int(len(lst) / w) + 1 14 | mean_cov = 0 15 | start = -1 16 | end = -1 17 | flag = False 18 | posi_list = [] 19 | for i in range(k): 20 | mean_cov = sum([int(posi_cov.strip().split()[1]) for posi_cov in lst[i * w:(i + 1) * w]]) / w 21 | # print('mean_cov:{}'.format(mean_cov)) 22 | if mean_cov < min_cov: 23 | if start != -1: 24 | if (end - start) >= min_len: 25 | posi_list.append((start, end, end - start)) 26 | start = -1 27 | end = -1 28 | flag = False 29 | continue 30 | elif mean_cov >= min_cov and not flag: 31 | start = int(lst[i * w].strip().split()[0]) 32 | end = int(lst[min(len(lst), (i + 1) * w) - 1].strip().split()[0]) 33 | flag = True 34 | elif flag: 35 | 36 | end = int(lst[min(len(lst), (i + 1) * w) - 1].strip().split()[0]) 37 | # print('end:{}'.format(end)) 38 | if flag and ((end - start) >= min_len): 39 | posi_list.append((start, end, end - start)) # add the last one 40 | return posi_list 41 | 42 | 43 | def cov_based_misasm_removal_xx(param): 44 | i, reads_fa,outdir, min_cov, threads, trim_ends = param 45 | contig_fa='contig.{}.fa'.format(i) 46 | os.system("mkdir -p {}".format(outdir)) 47 | out_fa = outdir + '/xx.{}.fa'.format(i) 48 | 49 | # compute coverage for each base, filter secondary and supplementary alignments at first 50 | bam = outdir + "/" + str(i) + ".bam" 51 | os.system("minimap2 -a" + " --secondary=no -t 1 " +" "+ contig_fa + " " + reads_fa + \ 52 | " 2>/dev/null |samtools view -hS -F 2048 -|samtools sort -@ 1 - >" + bam) 53 | # print("minimap2 -a" + " --secondary=no -t " + str(threads) + contig_fa + " " + reads_fa + \ 54 | # " 2>/dev/null |samtools view -hS -F 2048 -|samtools sort -@ 24 - >" + bam) 55 | 56 | # TODO need to consider the low coverage region in the middle of sequence 57 | # @@ check why many errors in the middle of some super reads: 58 | # some reads are assigned into a wrong haplotype group or reads in the middle are belong to 59 | # one haplotype and reads in both ends are belong to the other, which may cause this mistake, therefore, 60 | # one should split the super reads into different parts and only keep the short fragments 61 | # (OR only keep the longest fragment) that are satisfied with min coverage requirement. 62 | """ 63 | positions = os.popen("samtools depth " + bam + "|awk '$3>=" + str(min_cov) + \ 64 | "'|cut -f 2|sed -n '1p;$p'").read().strip() 65 | if positions: 66 | [start, end] = positions.split("\n") # 1 based for sam 67 | else: 68 | print("{}.{}: no sequence satisfies the requirement of min coverage".format(i, hap)) 69 | open(out_fa, 'w').close() # new an empty file 70 | return 71 | """ 72 | 73 | if os.path.getsize(bam) == 0: 74 | print("{}: no read can be aligned to the super read, skipping...".format(i), 75 | file=open("{}/log".format(outdir), 'a')) 76 | open(out_fa, 'w').close() # new an empty file 77 | return 78 | else: 79 | positions = os.popen("samtools depth " + bam + "|awk '{print NR, $0}'" + "|awk '$4>=" + str(min_cov) + \ 80 | "'|cut -f 1 -d ' '|sed -n '1p;$p'").read().strip() 81 | if positions: 82 | [a, b] = [int(x) for x in positions.split("\n")] # line number 83 | posi_cov_list = os.popen("samtools depth " + bam + "|cut -f 2,3").read().strip().split('\n') 84 | posi_list = get_posi_by_depth(posi_cov_list[(a - 1):b], min_cov, w=5) # 1 based for sam 85 | print('posi_list:{}'.format(posi_list)) 86 | else: 87 | print("contig.{}.fa: no sequence satisfies the requirement of min coverage".format(i), 88 | file=open("{}/log".format(outdir), 'a')) 89 | open(out_fa, 'w').close() # new an empty file 90 | return 91 | 92 | with open(contig_fa, "r") as fr: 93 | seq = fr.readline() 94 | seq = fr.readline().strip() 95 | 96 | sub_k = 1 97 | if trim_ends: 98 | for start, end, _ in posi_list: 99 | consensus = seq[(int(start) - 1):int(end)] 100 | if len(posi_list) == 1: 101 | head = ">c_{}\n".format(i) 102 | else: 103 | head = ">c_{}_sub{}\n".format(i, sub_k) 104 | with open(out_fa, 'a') as fw: 105 | fw.write(head + consensus + '\n') 106 | sub_k += 1 107 | else: 108 | # trim ends with low coverage and also break at the misassembled positions in the middle regions 109 | for start, end, _ in posi_list: 110 | if len(posi_list) == 1: 111 | consensus = seq[(int(start) - 1):int(end)] 112 | head = ">contig_{}\n".format(i) 113 | else: 114 | if sub_k == 1: 115 | consensus = seq[:int(end)] 116 | elif sub_k == len(posi_list): 117 | consensus = seq[(int(start) - 1):] 118 | else: 119 | consensus = seq[(int(start) - 1):int(end)] 120 | head = ">contig_{}_sub{}\n".format(i, sub_k) 121 | with open(outdir+'/contig.{}.sub{}.fa'.format(i,sub_k), 'w') as fw: 122 | fw.write(head + consensus + '\n') 123 | sub_k += 1 124 | 125 | return 126 | 127 | 128 | def cov_based_misasm_removal(param): 129 | i, reads_fa, all_bam, outdir, min_cov, threads, trim_ends = param 130 | # os.system("mkdir -p {}".format(outdir)) 131 | out_fa = outdir + '/xx.{}.fa'.format(i) 132 | 133 | 134 | # compute coverage for each base, filter secondary and supplementary alignments at first 135 | bam = outdir + "/contig" + str(i) + ".bam" 136 | os.system("samtools view -b {} contig{} >{}".format(all_bam,str(i),bam)) 137 | os.system("samtools index {}".format(bam)) 138 | 139 | if os.path.getsize(bam) == 0: 140 | print("contig{}: bam is empty, skipping...".format(i), 141 | file=open("{}/log".format(outdir), 'a')) 142 | open(out_fa, 'w').close() # new an empty file 143 | return 144 | else: 145 | positions = os.popen("samtools depth " + bam + "|awk '{print NR, $0}'" + "|awk '$4>=" + str(min_cov) + \ 146 | "'|cut -f 1 -d ' '|sed -n '1p;$p'").read().strip() 147 | if positions: 148 | [a, b] = [int(x) for x in positions.split("\n")] # line number 149 | posi_cov_list = os.popen("samtools depth " + bam + "|cut -f 2,3").read().strip().split('\n') 150 | posi_list = get_posi_by_depth(posi_cov_list[(a - 1):b], min_cov, w=5) # 1 based for sam 151 | print('# contig:{}, posi_list:{}'.format(i,posi_list)) 152 | else: 153 | print("contig.{}.fa: no sequence satisfies the requirement of min coverage".format(i), 154 | file=open("{}/log".format(outdir), 'a')) 155 | open(out_fa, 'w').close() # new an empty file 156 | return 157 | 158 | contig="contig{}".format(i) 159 | seq = contig2seq[contig] 160 | 161 | sub_k = 1 162 | if trim_ends: 163 | for start, end, _ in posi_list: 164 | consensus = seq[(int(start) - 1):int(end)] 165 | if len(posi_list) == 1: 166 | head = ">c_{}\n".format(i) 167 | else: 168 | head = ">c_{}_sub{}\n".format(i, sub_k) 169 | with open(out_fa, 'a') as fw: 170 | fw.write(head + consensus + '\n') 171 | sub_k += 1 172 | else: 173 | # trim ends with low coverage and also break at the misassembled positions in the middle regions 174 | for start, end, _ in posi_list: 175 | if len(posi_list) == 1: 176 | consensus = seq[(int(start) - 1):int(end)] 177 | head = ">contig_{}\n".format(i) 178 | else: 179 | if sub_k == 1: 180 | consensus = seq[:int(end)] 181 | elif sub_k == len(posi_list): 182 | consensus = seq[(int(start) - 1):] 183 | else: 184 | consensus = seq[(int(start) - 1):int(end)] 185 | head = ">contig_{}_sub{}\n".format(i, sub_k) 186 | with open(outdir + '/contig.{}.sub{}.fa'.format(i, sub_k), 'w') as fw: 187 | fw.write(head + consensus + '\n') 188 | sub_k += 1 189 | 190 | return 191 | 192 | 193 | def clip_based_misasm_removal(read_fa, hap_fa, threads, outdir, min_clip_count): 194 | '''remove misassemblies based on clipped read alignments''' 195 | os.system("mkdir -p {}".format(outdir)) 196 | sam = outdir + '/tmp.sam' 197 | min_clip_len = 5 198 | 199 | os.system( 200 | "minimap2 -a --secondary=no {} {} -t {}|samtools view -F 2048 - >{}".format(hap_fa, read_fa, threads, sam)) 201 | hap2bpposi_tmp = {} 202 | hap2bpposi = {} 203 | 204 | with open(sam, 'r') as fr: 205 | for line in fr: 206 | a = line.split() 207 | hap, posi, cigar, seq = a[2], int(a[3]), a[5], a[9] 208 | read_len = len(seq) 209 | bp_posi = -1 # breakpoint position at target sequence 210 | # split cigar into list of numbers and characters all separately 211 | splitcigar = ["".join(x) for _, x in itertools.groupby(cigar, key=str.isdigit)] # 25S100M2S 212 | if splitcigar[1] == 'S': # soft-clipped, left 213 | if int(splitcigar[0]) < min_clip_len: 214 | if splitcigar[-1] == 'S' and int(splitcigar[-2]) >= min_clip_len: 215 | bp_posi = posi + read_len - int(splitcigar[-2]) # soft-clipped on both sides,such as 2S1000M50S 216 | else: 217 | continue 218 | else: 219 | bp_posi = posi 220 | elif splitcigar[-1] == 'S': # soft-clipped, right 221 | if int(splitcigar[-2]) < min_clip_len: 222 | continue 223 | else: 224 | bp_posi = posi + read_len - int(splitcigar[-2]) 225 | else: 226 | continue 227 | if hap in hap2bpposi_tmp: 228 | hap2bpposi_tmp[hap].append(bp_posi) 229 | else: 230 | hap2bpposi_tmp[hap] = [bp_posi] 231 | 232 | for hap, posi_list in hap2bpposi_tmp.items(): 233 | posi2count = {} 234 | for posi in posi_list: 235 | if posi in posi2count: 236 | posi2count[posi] += 1 237 | else: 238 | posi2count[posi] = 1 239 | for posi, count in posi2count.items(): 240 | if count >= min_clip_count: 241 | print("## posi, count:{},{}".format(posi,count)) 242 | if hap in hap2bpposi: 243 | hap2bpposi[hap].append(posi) 244 | else: 245 | hap2bpposi[hap] = [posi] 246 | 247 | hap2bpposi = {hap: sorted(posi_list) for hap, posi_list in hap2bpposi.items()} 248 | print('## haplotype to breakpoint position: {}'.format(hap2bpposi)) 249 | hap='' 250 | out_info=[] 251 | i=0 252 | with open(hap_fa) as fr: 253 | for line in fr: 254 | sub_seqs=[] 255 | if line.startswith('>'): 256 | hap=line.strip()[1:] 257 | else: 258 | seq=line.strip() 259 | start=0 260 | if hap not in hap2bpposi: 261 | i+=1 262 | out_info.append('>hap_'+str(i)+'\n'+seq+'\n') 263 | continue 264 | for bp_posi in hap2bpposi[hap]: 265 | sub_seq = seq[start:(bp_posi-1)] 266 | sub_seqs.append(sub_seq) 267 | start=bp_posi-1 268 | 269 | sub_seqs.append(seq[start:]) #last sub sequence 270 | for s in sub_seqs: 271 | if len(s)<500: #filter short sequences 272 | continue 273 | i+=1 274 | out_info.append('>hap_' + str(i)+'\n'+s+'\n') 275 | 276 | with open(outdir+'/haplotypes.final.fasta','w') as fw: 277 | fw.write(''.join(out_info)) 278 | return 279 | 280 | 281 | 282 | 283 | if __name__ == '__main__': 284 | read_fa, contig_fa, outdir, threads, num_contigs= sys.argv[1:] 285 | min_cov=5 286 | 287 | contig2seq={} 288 | with open(contig_fa, 'r') as fr: 289 | for line in fr: 290 | if line.startswith('>'): 291 | contig = line.strip()[1:] 292 | else: 293 | contig2seq[contig] = line.strip() 294 | 295 | #map all corrected reads to all contigs 296 | os.system("mkdir -p {}".format(outdir)) 297 | bam = outdir+'/reads2contigs.bam' 298 | os.system("minimap2 -a" + " --secondary=no -t " +threads +" "+ contig_fa + " " + read_fa + \ 299 | " 2>/dev/null |samtools view -hS -F 2048 -|samtools sort -@ 8 - >" + bam) 300 | os.system("samtools index {}".format(bam)) 301 | 302 | 303 | print('## reads alingment finished.') 304 | params=[] 305 | for i in range(int(num_contigs)): 306 | params.append((str(i), read_fa,bam, outdir, min_cov,1,False)) 307 | # contig_fa = 'contig.{}.fa'.format(i) 308 | # cov_based_misasm_removal(str(i), read_fa, outdir, min_cov, threads=1) 309 | # clip_based_misasm_removal(read_fa,contig_fa , threads, outdir, min_clip_count=20) 310 | # clip_based_misasm_removal(read_fa, hap_fa, threads, outdir, min_clip_count=20) 311 | pool = Pool(int(threads)) 312 | res = pool.map(cov_based_misasm_removal, params, chunksize=1) 313 | 314 | pool.close() 315 | pool.join() -------------------------------------------------------------------------------- /src/rm_redundant_genomes.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | from itertools import combinations 4 | from multiprocessing import Pool 5 | 6 | '''This program is used to remove redundant genomes based on sequence divergence''' 7 | 8 | 9 | def fasta_len(fa): 10 | sum_len = 0 11 | with open(fa, 'r') as fr: 12 | for line in fr: 13 | if line.startswith('>'): 14 | continue 15 | else: 16 | sum_len += len(line.strip()) 17 | return sum_len 18 | 19 | 20 | def cal_genome_divergence(param): 21 | ## Full genome/assembly alignment, intra-species asm-to-asm alignment 22 | fa1, fa2,max_local_divergence,maxCO = param 23 | paf_out = os.popen("minimap2 -cx asm20 -t 1 {} {} 2>/dev/null".format(fa1, fa2)).read().strip().split('\n') 24 | divergence = 1.0 25 | contained = 0 # if it is contained contig or not 26 | 27 | if not paf_out[0]: 28 | return (fa1, fa2, divergence, divergence,contained) 29 | 30 | matched_len = 0 # identical bases 31 | ovlp_len = 0 # including mismatches and gaps 32 | for i, line in enumerate(paf_out): 33 | a = line.split('\t') 34 | matched_len += int(a[9]) 35 | ovlp_len += int(a[10]) 36 | fa1_len = fasta_len(fa1) 37 | fa2_len = fasta_len(fa2) 38 | 39 | # number of bases which are covered in fa1 or fa2, which can be also obtained from paf file 40 | # this may be != fa1_len - ovlp_len (because of indels in overlap) 41 | # and != fa1_len - matched_len (because of mismatch bases not involved in matched_len) 42 | fa1_uniq_ovlplen = os.popen("minimap2 -ax asm20 -t 1 {} {} 2>/dev/null|samtools sort - |samtools depth -|wc -l". 43 | format(fa1, fa2)).read().strip() 44 | fa2_uniq_ovlplen = os.popen("minimap2 -ax asm20 -t 1 {} {} 2>/dev/null|samtools sort - |samtools depth -|wc -l". 45 | format(fa2, fa1)).read().strip() 46 | fa1_uniq_ovlplen = int(fa1_uniq_ovlplen) 47 | fa2_uniq_ovlplen = int(fa2_uniq_ovlplen) 48 | # global_divergence = 1 - matched / (similar+different) 49 | global_divergence = round(1 - matched_len / (ovlp_len + fa1_len - fa1_uniq_ovlplen + fa2_len - fa2_uniq_ovlplen), 4) 50 | local_divergence = round(1 - matched_len / ovlp_len, 4) # only consider the overlap regions 51 | 52 | # Discard contained contigs 53 | fa1_oh = fa1_len - ovlp_len # general overhang length of fa1 54 | fa2_oh = fa2_len - ovlp_len 55 | # min_oh = 5, replaced with maxCO, maximum overhang len for contained contigs 56 | # max_local_divergence = 0.001 #pacbio clr 57 | # max_local_divergence = 0.01 #for test, maybe for ont TODO 58 | if fa1_oh <= maxCO and local_divergence < max_local_divergence: 59 | contained = 1 # fa1 is contained contig 60 | elif fa2_oh <= maxCO and local_divergence < max_local_divergence: 61 | contained = 2 62 | 63 | return (fa1, fa2, global_divergence, local_divergence, contained) 64 | 65 | 66 | if __name__ == '__main__': 67 | fa_list_file, max_global_divergence, outdir, threads,max_local_divergence,maxCO = sys.argv[1:] 68 | max_global_divergence = float(max_global_divergence) 69 | max_local_divergence = float(max_local_divergence) 70 | maxCO = int(maxCO) 71 | fa_list = [] 72 | with open(fa_list_file) as fr: 73 | for line in fr: 74 | fa_list.append(line.strip()) 75 | 76 | final_fastas = {fa: 1 for fa in fa_list} # the final fasta files after removing redundant genomes 77 | # print(final_fastas) 78 | div_out = [] 79 | params = [(fa1, fa2,max_local_divergence,maxCO) for fa1, fa2 in combinations(fa_list, 2)] 80 | 81 | pool = Pool(int(threads)) 82 | res = pool.map(cal_genome_divergence, params, chunksize=1) # ordered 83 | pool.close() 84 | pool.join() 85 | 86 | for line in res: 87 | # print('line:{}'.format(line)) 88 | fa1, fa2, global_divergence, local_divergence, contained = line 89 | div_out.append('\t'.join([fa1, fa2, str(global_divergence), str(local_divergence), str(contained)])) 90 | if contained == 1: 91 | if fa1 in final_fastas: 92 | del final_fastas[fa1] 93 | elif contained == 2: 94 | if fa2 in final_fastas: 95 | del final_fastas[fa2] 96 | elif global_divergence < max_global_divergence: 97 | if fasta_len(fa1) > fasta_len(fa2): 98 | if fa2 in final_fastas: 99 | del final_fastas[fa2] 100 | else: 101 | if fa1 in final_fastas: 102 | del final_fastas[fa1] 103 | 104 | with open(outdir + '/haplotypes_divergence.txt', 'w') as fw: 105 | fw.write('\n'.join(div_out) + '\n') 106 | 107 | i = 1 108 | with open(outdir + '/haplotypes.fa', 'w') as fw: 109 | for fa in final_fastas.keys(): 110 | with open(fa) as fr: 111 | for line in fr: 112 | if line.startswith('>'): 113 | continue 114 | else: 115 | if line.strip(): 116 | fw.write('>strain_{}\n'.format(i)) 117 | fw.write(line) 118 | i += 1 119 | -------------------------------------------------------------------------------- /src/sort_reads.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This script is used to sort long reads by read length and their overlaps, 3 | and unify the strand which all reads are from. 4 | ''' 5 | import os 6 | import sys 7 | # import networkx as nx 8 | 9 | from filter_ovlps import filter_ovlp 10 | 11 | 12 | def sort_reads_by_len(in_fa, outdir, top_k): 13 | id2seq = {} 14 | id = '' 15 | 16 | with open(in_fa) as fr: 17 | for line in fr: 18 | if line.startswith('>'): 19 | id = line.strip() 20 | else: 21 | seq = line.strip() 22 | id2seq[id] = [seq, len(seq)] 23 | 24 | out_fa = outdir + '/' + '.'.join(os.path.basename(in_fa).split('.')[:-1]) + '.top' + str(top_k) + '.fa' 25 | fw = open(out_fa, 'w') 26 | i = 0 27 | for id, val in sorted(id2seq.items(), key=lambda d: d[1][1], reverse=True): 28 | i += 1 29 | if i <= top_k: 30 | fw.write(id + '\n' + val[0] + '\n') 31 | return out_fa 32 | 33 | 34 | def cal_overlap(fasta, outdir, platform, threads, filter=True, min_ovlp_len=500, min_identity=0.0, o=200, r=0.8): 35 | ''' 36 | calculate the overlaps of long reads 37 | ''' 38 | paf = outdir + '/' + '.'.join(os.path.basename(fasta).split('.')[:-1]) + '.paf' 39 | filtered_paf = outdir + '/' + '.'.join(os.path.basename(fasta).split('.')[:-1]) + '.filtered.paf' 40 | if platform == 'pb': # TODO: -c is necessary or not? 41 | # minimap = "minimap2 -x ava-pb -Hk19 -Xw5 -m100 -g10000 --max-chain-skip 25 " + \ 42 | # "-t %s %s %s |cut -f 1-12 |fpa drop -i -m >%s" % (threads, fasta, fasta, paf) 43 | 44 | minimap = "minimap2 -x ava-pb -Hk19 -Xw5 -m100 -g10000 --max-chain-skip 25 -t {} \ 45 | {} {} 2>/dev/null |cut -f 1-12 |awk '$11>={} && $10/$11 >={} ' |fpa drop -i -m >{}" \ 46 | .format(threads, fasta, fasta, min_ovlp_len, min_identity, paf) 47 | 48 | elif platform == 'ont': 49 | # minimap = "minimap2 -x ava-ont -k15 -Xw5 -m100 -g10000 -r2000 --max-chain-skip 25 " + \ 50 | # "-t %s %s %s |cut -f 1-12 |fpa drop -i -m >%s" % (threads, fasta, fasta, paf) 51 | minimap = "minimap2 -x ava-ont -k15 -Xw5 -m100 -g10000 -r2000 --max-chain-skip 25 -t {} \ 52 | {} {} 2>/dev/null |cut -f 1-12 |awk '$11>={} && $10/$11 >={} ' |fpa drop -i -m >{}" \ 53 | .format(threads, fasta, fasta, min_ovlp_len, min_identity, paf) 54 | else: 55 | raise Exception('wrong platform for reads type: pb/ont') 56 | os.system(minimap) 57 | if filter: 58 | filter_ovlp(paf, filtered_paf, min_ovlp_len, min_identity, o, r) 59 | return filtered_paf 60 | return paf 61 | 62 | 63 | def count_lost_reads(fa, paf): 64 | num_reads = int(os.popen('cat {}|grep ">"|wc -l'.format(fa)).read()) 65 | reads_in_ovlps = {} 66 | with open(paf) as fr: 67 | for line in fr: 68 | a = line.split() 69 | reads_in_ovlps[a[0]] = 1 70 | reads_in_ovlps[a[5]] = 1 71 | print('\n\nraw reads: {}, {} reads in filtered overlaps\n'.format(num_reads, len(reads_in_ovlps))) 72 | return num_reads, len(reads_in_ovlps) 73 | 74 | 75 | def reverse_comp(seq): 76 | base2comp = {'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G', 'N': 'N', 77 | 'a': 't', 't': 'a', 'g': 'c', 'c': 'g', 'n': 'n'} 78 | rev_seq = seq[::-1] 79 | rev_comp_seq = ''.join([base2comp[base] for base in rev_seq]) 80 | return rev_comp_seq 81 | 82 | 83 | def paf2graph(paf): 84 | edges = [] 85 | with open(paf) as fr: 86 | for line in fr: 87 | a = line.strip().split() 88 | edges.append((a[0], a[5])) 89 | G = nx.Graph() 90 | G.add_edges_from(edges) 91 | return G 92 | 93 | 94 | def sort_reads_by_ovlps(fa, paf, outdir, by_length): 95 | '''sort reads by length, keep the next read has overlap with the previous reads 96 | and unify the strand of all reads, assume '+' 97 | ''' 98 | out_fa = outdir + '/' + '.'.join(os.path.basename(fa).split('.')[:-1]) + '.sorted.fa' 99 | if os.path.getsize(paf) == 0: 100 | os.system('cp {} {}'.format(fa,out_fa)) 101 | return out_fa 102 | 103 | fw = open(out_fa, 'w') 104 | rr2strand = {} # {read_i} {read_j} = '+' 105 | with open(paf, 'r') as fr: 106 | for line in fr: 107 | a = line.split() 108 | rr2strand.setdefault(a[0], {})[a[5]] = a[4] 109 | rr2strand.setdefault(a[5], {})[a[0]] = a[4] 110 | 111 | i = 0 112 | read2strand = {} 113 | used_reads = {} 114 | read2seq = {} 115 | first_read = '' 116 | with open(fa) as fr: 117 | read = '' 118 | for line in fr: 119 | if line.startswith('>'): 120 | i += 1 121 | read = line.strip().split()[0][1:] 122 | if i == 1: 123 | first_read = read 124 | read2strand[first_read] = '+' 125 | else: 126 | seq = line.strip() 127 | read2seq[read] = [seq, len(seq)] 128 | 129 | # output the first read info 130 | fw.write('>' + first_read + '\n' + read2seq[first_read][0] + '\n') 131 | 132 | used_reads[first_read] = 1 133 | reverse = 0 # count number of reads which need to reverse_complement 134 | read_i = 1 135 | next_read = first_read 136 | neighbors = {} 137 | neighbors[next_read] = 1 138 | while True: 139 | read_i += 1 140 | if read_i % 1000 == 0: 141 | print('processing the {} read...'.format(read_i)) 142 | 143 | for r in rr2strand[next_read].keys(): 144 | if r not in used_reads: 145 | neighbors[r] = 1 146 | del neighbors[next_read] 147 | # sort neighbor reads by length 148 | if by_length: 149 | if len(neighbors) > 0: 150 | next_read = sorted(neighbors.keys(), key=lambda r: read2seq[r][1], reverse=True)[0] 151 | else: 152 | raise Exception('No overlap found for read because the overlap graph is not connected !') 153 | else: 154 | for read in neighbors.keys(): 155 | next_read = read 156 | break 157 | 158 | # check strand of reads 159 | for used_read in used_reads.keys(): 160 | if used_read in rr2strand[next_read]: 161 | ovlp_strand = rr2strand[next_read][used_read] 162 | if read2strand[used_read] == '+': 163 | if ovlp_strand == '+': 164 | read2strand[next_read] = '+' 165 | else: 166 | read2strand[next_read] = '-' 167 | elif read2strand[used_read] == '-': 168 | if ovlp_strand == '+': 169 | read2strand[next_read] = '-' 170 | else: 171 | read2strand[next_read] = '+' 172 | break 173 | # TODO: check strand conflicts ? 174 | 175 | if read2strand[next_read] == '+': 176 | fw.write('>' + next_read + '\n' + read2seq[next_read][0] + '\n') 177 | elif read2strand[next_read] == '-': 178 | reverse += 1 179 | fw.write('>' + next_read + '\n' + reverse_comp(read2seq[next_read][0]) + '\n') 180 | else: 181 | raise Exception('Wrong read strand found: {}'.format(read2strand[next_read])) 182 | used_reads[next_read] = 1 183 | if len(used_reads) == len(rr2strand): # all reads in overlap file are visited 184 | break 185 | fw.close() 186 | print('number of reverse_complement reads: {}'.format(reverse)) 187 | return out_fa 188 | 189 | 190 | if __name__ == '__main__': 191 | # fa = 'data/hiv/reads.fa' 192 | # outdir = 'data/hiv' 193 | # top_k=100 194 | fa, outdir, top_k, by_length, platform = sys.argv[1:] 195 | top_fa = sort_reads_by_len(fa, outdir, int(top_k)) 196 | 197 | #mean read length=2.4kb, min_ovlp_len=400 in the first version 198 | paf = cal_overlap(top_fa, outdir, platform, threads=40, filter=True, min_ovlp_len=1000, 199 | min_identity=0.1, o=400, r=0.8) 200 | count_lost_reads(top_fa, paf) 201 | 202 | # check if overlap graph is connected TODO: add a reference as the first read ? 203 | # G = paf2graph(paf) 204 | # if nx.is_connected(G): 205 | # print('The overlap graph is connected.') 206 | # else: 207 | # n = nx.algorithms.components.number_connected_components(G) 208 | # print('The overlap graph is not connected and has {} components,\n' 209 | # 'Refiltering overlaps with more relaxed threshold is recommended.\n\n'.format(n)) 210 | 211 | # run anyway 212 | sort_reads_by_ovlps(top_fa, paf, outdir, int(by_length)) 213 | -------------------------------------------------------------------------------- /src/strainline.only_iter.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | #Prints a help message 6 | function print_help() { 7 | echo "Usage: $0 [options] -i reads.fasta -o out/ -p sequencingPlatform" 8 | echo "" 9 | echo "Full-length De Novo Viral Haplotype Reconstruction from Noisy Long Reads" 10 | echo "" 11 | echo "Author: Xiao Luo" 12 | echo "Date: Mar 2021" 13 | echo "" 14 | echo " Input:" 15 | echo " reads.fasta: fasta file of input long reads." 16 | echo " out/: directory where to output the results." 17 | echo " sequencingPlatform: long read sequencing platform: PacBio (-p pb) or Oxford Nanopore (-p ont)" 18 | echo "" 19 | echo " Options:" 20 | echo " --minTrimmedLen INT: Minimum trimmed read length. (default: 1000)" 21 | echo " --topk INT, -k INT: Choose top k seed reads. (default: 50)" 22 | echo " --minOvlpLen INT: Minimum read overlap length. (default: 1000)" 23 | echo " --minIdentity FLOAT: Minimum identity of overlaps. (default: 0.99)" 24 | echo " --minSeedLen INT: Minimum seed read length. (default: 3000)" 25 | echo " --maxOH INT: Maximum overhang length allowed for overlaps. (default: 30)" 26 | echo " --iter INT: Number of iterations for contig extension. (default: 2)" 27 | echo " --from INT: From this iteration for further contig extension. (default: 2)" 28 | echo " --maxGD FLOAT: Maximum global divergence allowed for merging haplotypes. (default: 0.01)" 29 | echo " --maxLD FLOAT: Maximum local divergence allowed for merging haplotypes. (default: 0.001)" 30 | echo " --maxCO INT: Maximum overhang length allowed for contig contains. (default: 5)" 31 | # echo " --perIdentity INT: Percent identity for haplotype abundacne computation. (default: 97)" 32 | echo " --minAbun FLOAT: Minimum abundance for filtering haplotypes (default: 0.02)" 33 | echo " --rmMisassembly BOOL: Break contigs at potential misassembled positions (default: False)" 34 | echo " --correctErr BOOL: Perform error correction for input reads (default: True)" 35 | echo " --threads INT, -t INT: Number of processes to run in parallel (default: 8)." 36 | echo " --help, -h: Print this help message." 37 | exit 1 38 | } 39 | 40 | #Set options to default values 41 | input_fa="" 42 | threads=8 43 | outdir="out/" 44 | 45 | min_trimmed_len=1000 46 | 47 | topk=50 48 | platform="pb" 49 | min_ovlp_len=1000 50 | min_identity=0.99 51 | o=30 52 | r=0.8 53 | #max_ovlps=10000 #TODO,reads are already corrected, so maybe use 1000? 54 | max_ovlps=1000 55 | min_sread_len=3000 56 | 57 | iter=2 58 | max_global_divergence=0.01 59 | 60 | #TODO: add if else 61 | max_local_divergence=0.001 #for CLR, 0.01 for ONT. #SARS-CoV-2 / (5-HIV different depth) results in paper use 0.01, 62 | maxCO=5 63 | 64 | percent_identity=97 65 | min_abun=0.02 # 66 | rm_misassembly="False" 67 | correct_err="True" 68 | 69 | #Print help if no argument specified 70 | if [[ "$1" == "" ]]; then 71 | print_help 72 | fi 73 | 74 | #Options handling 75 | while [[ "$1" != "" ]]; do 76 | case "$1" in 77 | "--help" | "-h") 78 | print_help 79 | ;; 80 | "-i") 81 | case "$2" in 82 | "") 83 | echo "Error: $1 expects an argument" 84 | exit 1 85 | ;; 86 | *) 87 | input_fa="$2" 88 | shift 2 89 | ;; 90 | esac 91 | ;; 92 | "-o") 93 | case "$2" in 94 | "") 95 | echo "Error: $1 expects an argument" 96 | exit 1 97 | ;; 98 | *) 99 | outdir="$2" 100 | shift 2 101 | ;; 102 | esac 103 | ;; 104 | "-p") 105 | case "$2" in 106 | "") 107 | echo "Error: $1 expects an argument" 108 | exit 1 109 | ;; 110 | *) if [[ "$2" == "pb" ]]; then 111 | platform="pb" 112 | shift 2 113 | elif [[ "$2" == "ont" ]]; then 114 | platform="ont" 115 | shift 2 116 | else 117 | echo "Error: $1 must be either pb or ont" 118 | exit 1 119 | fi ;; 120 | esac 121 | ;; 122 | "--minTrimmedLen") 123 | case "$2" in 124 | "") 125 | echo "Error: $1 expects an argument" 126 | exit 1 127 | ;; 128 | *) 129 | min_trimmed_len="$2" 130 | shift 2 131 | ;; 132 | esac 133 | ;; 134 | "--topk" | "-k") 135 | case "$2" in 136 | "") 137 | echo "Error: $1 expects an argument" 138 | exit 1 139 | ;; 140 | *) 141 | topk="$2" 142 | shift 2 143 | ;; 144 | esac 145 | ;; 146 | "--minOvlpLen") 147 | case "$2" in 148 | "") 149 | echo "Error: $1 expects an argument" 150 | exit 1 151 | ;; 152 | *) 153 | min_ovlp_len="$2" 154 | shift 2 155 | ;; 156 | esac 157 | ;; 158 | "--minIdentity") 159 | case "$2" in 160 | "") 161 | echo "Error: $1 expects an argument" 162 | exit 1 163 | ;; 164 | *) 165 | min_identity="$2" 166 | shift 2 167 | ;; 168 | esac 169 | ;; 170 | "--minSeedLen") 171 | case "$2" in 172 | "") 173 | echo "Error: $1 expects an argument" 174 | exit 1 175 | ;; 176 | *) 177 | min_sread_len="$2" 178 | shift 2 179 | ;; 180 | esac 181 | ;; 182 | "--maxOH") 183 | case "$2" in 184 | "") 185 | echo "Error: $1 expects an argument" 186 | exit 1 187 | ;; 188 | *) 189 | o="$2" 190 | shift 2 191 | ;; 192 | esac 193 | ;; 194 | "--iter") 195 | case "$2" in 196 | "") 197 | echo "Error: $1 expects an argument" 198 | exit 1 199 | ;; 200 | *) 201 | iter="$2" 202 | shift 2 203 | ;; 204 | esac 205 | ;; 206 | "--from") 207 | case "$2" in 208 | "") 209 | echo "Error: $1 expects an argument" 210 | exit 1 211 | ;; 212 | *) 213 | from="$2" 214 | shift 2 215 | ;; 216 | esac 217 | ;; 218 | "--maxGD") 219 | case "$2" in 220 | "") 221 | echo "Error: $1 expects an argument" 222 | exit 1 223 | ;; 224 | *) 225 | max_global_divergence="$2" 226 | shift 2 227 | ;; 228 | esac 229 | ;; 230 | "--maxLD") 231 | case "$2" in 232 | "") 233 | echo "Error: $1 expects an argument" 234 | exit 1 235 | ;; 236 | *) 237 | max_local_divergence="$2" 238 | shift 2 239 | ;; 240 | esac 241 | ;; 242 | "--maxCO") 243 | case "$2" in 244 | "") 245 | echo "Error: $1 expects an argument" 246 | exit 1 247 | ;; 248 | *) 249 | maxCO="$2" 250 | shift 2 251 | ;; 252 | esac 253 | ;; 254 | "--minAbun") 255 | case "$2" in 256 | "") 257 | echo "Error: $1 expects an argument" 258 | exit 1 259 | ;; 260 | *) 261 | min_abun="$2" 262 | shift 2 263 | ;; 264 | esac 265 | ;; 266 | "--rmMisassembly") 267 | case "$2" in 268 | "") 269 | echo "Error: $1 expects an argument" 270 | exit 1 271 | ;; 272 | *) 273 | rm_misassembly="$2" 274 | shift 2 275 | ;; 276 | esac 277 | ;; 278 | "--correctErr") 279 | case "$2" in 280 | "") 281 | echo "Error: $1 expects an argument" 282 | exit 1 283 | ;; 284 | *) 285 | correct_err="$2" 286 | shift 2 287 | ;; 288 | esac 289 | ;; 290 | "--threads" | "-t") 291 | case "$2" in 292 | "") 293 | echo "Error: $1 expects an argument" 294 | exit 1 295 | ;; 296 | *) 297 | threads="$2" 298 | shift 2 299 | ;; 300 | esac 301 | ;; 302 | # 303 | --) 304 | shift 305 | break 306 | ;; 307 | *) 308 | echo "Error: invalid option \"$1\"" 309 | exit 1 310 | ;; 311 | esac 312 | done 313 | 314 | #Exit if no input or no output files have been specified 315 | if [[ $input_fa == "" ]]; then 316 | echo "Error: -i must be specified" 317 | exit 1 318 | fi 319 | 320 | basepath=$(dirname $0) 321 | 322 | ############################################## 323 | ######## Step1: read error correction ######## 324 | ############################################## 325 | #if [ ! -d $outdir ]; then 326 | # mkdir $outdir 327 | #else 328 | # echo Directory \'$outdir\' 'already exists, please use a new one, exiting...' 329 | # exit 330 | #fi 331 | mkdir -p $outdir 332 | 333 | input_fa=$(readlink -f $input_fa) 334 | cd $outdir || exit 335 | 336 | ln -fs $input_fa reads.fasta 337 | 338 | if [[ $correct_err == "True" ]];then 339 | fasta2DAM reads.dam reads 340 | DBsplit -s256 -x$min_trimmed_len reads.dam # -x: Trimmed DB has reads >= this threshold. 341 | mkdir tmp || exit 342 | #HPC.daligner reads.dam -T$threads | bash #return core-dump error if using all cores 343 | HPC.daligner reads.dam -e0.85 -P./tmp -T$threads | bash 344 | # daccord -t$threads reads.las reads.dam >corrected.0.fa 345 | touch corrected.0.fa 346 | for las_file in reads.*las; 347 | do 348 | daccord -t$threads $las_file reads.dam >>corrected.0.fa 349 | done 350 | 351 | echo 'Step1: read error correction. Finished.' 352 | else 353 | echo 'Skip Step1, do not perform error correction.' 354 | fi 355 | 356 | ############################################## 357 | ########### Step2: read clustering ########### 358 | ############################################## 359 | 360 | for ((i = $from; i < $iter; i++)); do 361 | let j=$i+1 362 | mkdir -p iter$j 363 | #reformat fasta 364 | python $basepath/reformat_fa.py corrected.$i.fa corrected.$i.reformat.fa 365 | python $basepath/clustering.py corrected.$i.reformat.fa ./iter$j/ $topk $platform $threads $min_ovlp_len $min_identity $o $r $max_ovlps $min_sread_len 366 | cat ./iter$j/contig.*.fa | perl -ne 'if (/^>/){print ">r$.\n";}else{print;}' >corrected.$j.fa 367 | done 368 | 369 | python $basepath/reformat_fa.py corrected.$j.fa contigs.fa 370 | 371 | ############################################## 372 | #### Step3: redundant haplotypes removal ##### 373 | ############################################## 374 | 375 | ls ./iter$j/contig.*.fa >contig_list.txt 376 | 377 | fa_list_file=contig_list.txt 378 | 379 | #generate 'haplotypes.fa' 380 | python $basepath/rm_redundant_genomes.py $fa_list_file $max_global_divergence . $threads $max_local_divergence $maxCO 381 | 382 | 383 | #optional ,remove misassembly 384 | if [[ $rm_misassembly == "True" ]]; then 385 | cat haplotypes.fa|perl -ne 'BEGIN{$head;$i=0;}if(/^>/){$head=">contig$i";}else{if (length($_) >1000){print "$head\n$_";$i+=1;} }' >tmp.fa 386 | num_contig=`cat tmp.fa |grep ">"|wc -l` 387 | fa_read=corrected.0.fa #corrected reads 388 | python $basepath/rm_misassembly.py $fa_read tmp.fa rmMisassemly $threads $num_contig 389 | cat rmMisassemly/contig.*.fa >haplotypes.rm_misassembly.redundant.fa 390 | 391 | #remove redundant contigs again because some non-redundant contigs may be caused by misassembly 392 | ls rmMisassemly/contig.*.fa >contig_list.txt2 393 | python $basepath/rm_redundant_genomes.py contig_list.txt2 $max_global_divergence . $threads $max_local_divergence $maxCO 394 | cp haplotypes.fa haplotypes.rm_misassembly.fa 395 | elif [[ $rm_misassembly == "False" ]]; then 396 | cp haplotypes.fa haplotypes.rm_misassembly.fa 397 | else 398 | echo "invalid value for --rmMisassembly, should be either True or False." 399 | exit 1 400 | fi 401 | 402 | ############################################## 403 | ### Step4: low frequent haplotypes removal ### 404 | ############################################## 405 | export min_abundance=$min_abun 406 | 407 | mkdir -p filter_by_abun 408 | cd filter_by_abun || exit 409 | fa_read=../corrected.0.fa #corrected reads 410 | 411 | for i in {1..2}; 412 | do 413 | if [[ $i == 1 ]];then 414 | cat ../haplotypes.rm_misassembly.fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 415 | else 416 | cat haplotypes.final.fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 417 | fi 418 | minimap2 -a --secondary=no -t $threads haps.fa $fa_read | samtools view -F 3584 -b -t $threads | samtools sort - >haps.bam 419 | 420 | jgi_summarize_bam_contig_depths haps.bam --percentIdentity $percent_identity --outputDepth haps.depth 421 | 422 | perl -e 'open A,"haps.depth";;my$alldepth=0;while(){my@a=split;$alldepth+=$a[2];}close A; \ 423 | open A,"haps.depth";;while(){my@a=split;my$d=$a[2]/$alldepth;print "$a[0]\t$a[1]\t$a[2]\t$d\n";}close A; ' | 424 | sort -k4nr >haps.depth.sort 425 | 426 | perl -e ' my%id2seq;$/=">";open A,"haps.fa";;while(){chomp;my@a=split;$id2seq{$a[0]}=$a[1];}close A; 427 | $/="\n"; open A,"haps.depth.sort";my$k=0;while(){chomp;my@a=split;next if $a[-1]<$ENV{"min_abundance"}; 428 | my$abun=sprintf "%.3f",$a[-1];my$cov=sprintf "%.0f",$a[-2]; $k+=1;print ">hap$k $cov"."x freq=$abun\n$id2seq{$a[0]}\n";}close A; ' >haplotypes.final.fa 429 | done 430 | #TODO, after filtering, is it necessary to recompute the abundance of haplotypes ? yes, see for loop 431 | 432 | 433 | ## Done ## 434 | cd .. 435 | ln -fs ./filter_by_abun/haplotypes.final.fa . 436 | echo 'All steps finished successfully.' 437 | echo 'The final haplotypes and their relative abundances are saved here: '$outdir'/haplotypes.final.fa' 438 | -------------------------------------------------------------------------------- /src/strainline.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -e 4 | 5 | #Prints a help message 6 | function print_help() { 7 | echo "Usage: $0 [options] -i reads.fasta -o out/ -p sequencingPlatform" 8 | echo "" 9 | echo "Full-length De Novo Viral Haplotype Reconstruction from Noisy Long Reads" 10 | echo "" 11 | echo "Author: Xiao Luo" 12 | echo "Date: Mar 2021" 13 | echo "" 14 | echo " Input:" 15 | echo " reads.fasta: fasta file of input long reads." 16 | echo " out/: directory where to output the results." 17 | echo " sequencingPlatform: long read sequencing platform: PacBio (-p pb) or Oxford Nanopore (-p ont)" 18 | echo "" 19 | echo " Options:" 20 | echo " --minTrimmedLen INT: Minimum trimmed read length. (default: 1000)" 21 | echo " --topk INT, -k INT: Choose top k seed reads. (default: 100)" 22 | echo " --minOvlpLen INT: Minimum read overlap length. (default: 1000)" 23 | echo " --minIdentity FLOAT: Minimum identity of overlaps. (default: 0.99)" 24 | echo " --minSeedLen INT: Minimum seed read length. (default: 3000)" 25 | echo " --maxOH INT: Maximum overhang length allowed for overlaps. (default: 30)" 26 | echo " --iter INT: Number of iterations for contig extension. (default: 2)" 27 | echo " --maxGD FLOAT: Maximum global divergence allowed for merging haplotypes. (default: 0.01)" 28 | echo " --maxLD FLOAT: Maximum local divergence allowed for merging haplotypes. (default: 0.001)" 29 | echo " --maxCO INT: Maximum overhang length allowed for contig contains. (default: 5)" 30 | # echo " --perIdentity INT: Percent identity for haplotype abundacne computation. (default: 97)" 31 | echo " --minAbun FLOAT: Minimum abundance for filtering haplotypes (default: 0.02)" 32 | echo " --rmMisassembly BOOL: Break contigs at potential misassembled positions (default: False)" 33 | echo " --correctErr BOOL: Perform error correction for input reads (default: True)" 34 | echo " --dsim FLOAT: Look for alignments with this percent similarity in Daligner. (default: 0.85)" 35 | echo " --threads INT, -t INT: Number of processes to run in parallel (default: 8)." 36 | echo " --help, -h: Print this help message." 37 | exit 1 38 | } 39 | 40 | #Set options to default values 41 | input_fa="" 42 | threads=8 43 | outdir="out/" 44 | 45 | min_trimmed_len=1000 46 | 47 | topk=100 48 | platform="pb" 49 | min_ovlp_len=1000 50 | min_identity=0.99 51 | o=30 52 | r=0.8 53 | max_ovlps=1000 54 | min_sread_len=3000 55 | 56 | iter=2 57 | max_global_divergence=0.01 58 | max_local_divergence=0.001 59 | maxCO=5 60 | 61 | percent_identity=97 62 | min_abun=0.02 # 63 | rm_misassembly="False" 64 | correct_err="True" 65 | dsim=0.85 66 | 67 | #Print help if no argument specified 68 | if [[ "$1" == "" ]]; then 69 | print_help 70 | fi 71 | 72 | #Options handling 73 | while [[ "$1" != "" ]]; do 74 | case "$1" in 75 | "--help" | "-h") 76 | print_help 77 | ;; 78 | "-i") 79 | case "$2" in 80 | "") 81 | echo "Error: $1 expects an argument" 82 | exit 1 83 | ;; 84 | *) 85 | input_fa="$2" 86 | shift 2 87 | ;; 88 | esac 89 | ;; 90 | "-o") 91 | case "$2" in 92 | "") 93 | echo "Error: $1 expects an argument" 94 | exit 1 95 | ;; 96 | *) 97 | outdir="$2" 98 | shift 2 99 | ;; 100 | esac 101 | ;; 102 | "-p") 103 | case "$2" in 104 | "") 105 | echo "Error: $1 expects an argument" 106 | exit 1 107 | ;; 108 | *) if [[ "$2" == "pb" ]]; then 109 | platform="pb" 110 | shift 2 111 | elif [[ "$2" == "ont" ]]; then 112 | platform="ont" 113 | shift 2 114 | else 115 | echo "Error: $1 must be either pb or ont" 116 | exit 1 117 | fi ;; 118 | esac 119 | ;; 120 | "--minTrimmedLen") 121 | case "$2" in 122 | "") 123 | echo "Error: $1 expects an argument" 124 | exit 1 125 | ;; 126 | *) 127 | min_trimmed_len="$2" 128 | shift 2 129 | ;; 130 | esac 131 | ;; 132 | "--topk" | "-k") 133 | case "$2" in 134 | "") 135 | echo "Error: $1 expects an argument" 136 | exit 1 137 | ;; 138 | *) 139 | topk="$2" 140 | shift 2 141 | ;; 142 | esac 143 | ;; 144 | "--minOvlpLen") 145 | case "$2" in 146 | "") 147 | echo "Error: $1 expects an argument" 148 | exit 1 149 | ;; 150 | *) 151 | min_ovlp_len="$2" 152 | shift 2 153 | ;; 154 | esac 155 | ;; 156 | "--minIdentity") 157 | case "$2" in 158 | "") 159 | echo "Error: $1 expects an argument" 160 | exit 1 161 | ;; 162 | *) 163 | min_identity="$2" 164 | shift 2 165 | ;; 166 | esac 167 | ;; 168 | "--minSeedLen") 169 | case "$2" in 170 | "") 171 | echo "Error: $1 expects an argument" 172 | exit 1 173 | ;; 174 | *) 175 | min_sread_len="$2" 176 | shift 2 177 | ;; 178 | esac 179 | ;; 180 | "--maxOH") 181 | case "$2" in 182 | "") 183 | echo "Error: $1 expects an argument" 184 | exit 1 185 | ;; 186 | *) 187 | o="$2" 188 | shift 2 189 | ;; 190 | esac 191 | ;; 192 | "--iter") 193 | case "$2" in 194 | "") 195 | echo "Error: $1 expects an argument" 196 | exit 1 197 | ;; 198 | *) 199 | iter="$2" 200 | shift 2 201 | ;; 202 | esac 203 | ;; 204 | "--maxGD") 205 | case "$2" in 206 | "") 207 | echo "Error: $1 expects an argument" 208 | exit 1 209 | ;; 210 | *) 211 | max_global_divergence="$2" 212 | shift 2 213 | ;; 214 | esac 215 | ;; 216 | "--maxLD") 217 | case "$2" in 218 | "") 219 | echo "Error: $1 expects an argument" 220 | exit 1 221 | ;; 222 | *) 223 | max_local_divergence="$2" 224 | shift 2 225 | ;; 226 | esac 227 | ;; 228 | "--maxCO") 229 | case "$2" in 230 | "") 231 | echo "Error: $1 expects an argument" 232 | exit 1 233 | ;; 234 | *) 235 | maxCO="$2" 236 | shift 2 237 | ;; 238 | esac 239 | ;; 240 | "--minAbun") 241 | case "$2" in 242 | "") 243 | echo "Error: $1 expects an argument" 244 | exit 1 245 | ;; 246 | *) 247 | min_abun="$2" 248 | shift 2 249 | ;; 250 | esac 251 | ;; 252 | "--rmMisassembly") 253 | case "$2" in 254 | "") 255 | echo "Error: $1 expects an argument" 256 | exit 1 257 | ;; 258 | *) 259 | rm_misassembly="$2" 260 | shift 2 261 | ;; 262 | esac 263 | ;; 264 | "--correctErr") 265 | case "$2" in 266 | "") 267 | echo "Error: $1 expects an argument" 268 | exit 1 269 | ;; 270 | *) 271 | correct_err="$2" 272 | shift 2 273 | ;; 274 | esac 275 | ;; 276 | "--dsim") 277 | case "$2" in 278 | "") 279 | echo "Error: $1 expects an argument" 280 | exit 1 281 | ;; 282 | *) 283 | dsim="$2" 284 | shift 2 285 | ;; 286 | esac 287 | ;; 288 | "--threads" | "-t") 289 | case "$2" in 290 | "") 291 | echo "Error: $1 expects an argument" 292 | exit 1 293 | ;; 294 | *) 295 | threads="$2" 296 | shift 2 297 | ;; 298 | esac 299 | ;; 300 | # 301 | --) 302 | shift 303 | break 304 | ;; 305 | *) 306 | echo "Error: invalid option \"$1\"" 307 | exit 1 308 | ;; 309 | esac 310 | done 311 | 312 | #Exit if no input or no output files have been specified 313 | if [[ $input_fa == "" ]]; then 314 | echo "Error: -i must be specified" 315 | exit 1 316 | fi 317 | 318 | basepath="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )" 319 | 320 | ############################################## 321 | ######## Step1: read error correction ######## 322 | ############################################## 323 | #if [ ! -d $outdir ]; then 324 | # mkdir $outdir 325 | #else 326 | # echo Directory \'$outdir\' 'already exists, please use a new one, exiting...' 327 | # exit 328 | #fi 329 | mkdir -p $outdir 330 | 331 | input_fa=$(readlink -f $input_fa) 332 | cd $outdir || exit 333 | 334 | # ln -fs $input_fa reads.fasta 335 | 336 | # convert input fasta into wrapped fasta (60 bases per line) 337 | # to avoid issue: 'Fasta line is too long' 338 | perl -e ' $/=">";open A,$ARGV[0] or die $!; ; 339 | while(){chomp;my@a=split/\n/;print ">$a[0]\n";my$seq=join("",@a[1..$#a]); 340 | my$len=length($seq);my $L=int $len/60;$L+=1 if $L*60<$len; 341 | for(my$i=0;$i<$L;$i++){print substr($seq,$i*60,60);print "\n";} 342 | }close A;' $input_fa >reads.fasta 343 | 344 | if [[ $correct_err == "True" ]];then 345 | fasta2DAM reads.dam reads 346 | DBsplit -s256 -x$min_trimmed_len reads.dam # -x: Trimmed DB has reads >= this threshold. 347 | # mkdir tmp || exit 348 | # #HPC.daligner reads.dam -T$threads | bash #return core-dump error if using all cores 349 | # HPC.daligner reads.dam -e$dsim -P./tmp -T$threads | bash #only conda installed version support '-P' 350 | HPC.daligner reads.dam -e$dsim -T$threads -l$min_ovlp_len | bash 351 | # HPC.daligner reads.dam -e0.85 -T$threads | bash 352 | # daccord -t$threads reads.las reads.dam >corrected.0.fa 353 | touch corrected.0.fa 354 | for las_file in reads.*las; 355 | do 356 | daccord -t$threads $las_file reads.dam >>corrected.0.fa 357 | done 358 | 359 | echo 'Step1: read error correction. Finished.' 360 | else 361 | echo 'Skip Step1, do not perform error correction.' 362 | fi 363 | 364 | ############################################## 365 | ########### Step2: read clustering ########### 366 | ############################################## 367 | 368 | for ((i = 0; i < $iter; i++)); do 369 | let j=$i+1 370 | mkdir -p iter$j 371 | #reformat fasta 372 | python $basepath/reformat_fa.py corrected.$i.fa corrected.$i.reformat.fa 373 | python $basepath/clustering.py corrected.$i.reformat.fa ./iter$j/ $topk $platform $threads $min_ovlp_len $min_identity $o $r $max_ovlps $min_sread_len 374 | cat ./iter$j/contig.*.fa | perl -ne 'if (/^>/){print ">r$.\n";}else{print;}' >corrected.$j.fa 375 | done 376 | 377 | python $basepath/reformat_fa.py corrected.$j.fa contigs.fa 378 | 379 | ############################################## 380 | #### Step3: redundant haplotypes removal ##### 381 | ############################################## 382 | 383 | ls ./iter$j/contig.*.fa >contig_list.txt 384 | 385 | fa_list_file=contig_list.txt 386 | 387 | #generate 'haplotypes.fa' 388 | python $basepath/rm_redundant_genomes.py $fa_list_file $max_global_divergence . $threads $max_local_divergence $maxCO 389 | 390 | 391 | #optional ,remove misassembly 392 | if [[ $rm_misassembly == "True" ]]; then 393 | cat haplotypes.fa|perl -ne 'BEGIN{$head;$i=0;}if(/^>/){$head=">contig$i";}else{if (length($_) >1000){print "$head\n$_";$i+=1;} }' >tmp.fa 394 | num_contig=`cat tmp.fa |grep ">"|wc -l` 395 | fa_read=corrected.0.fa #corrected reads 396 | python $basepath/rm_misassembly.py $fa_read tmp.fa rmMisassemly $threads $num_contig 397 | cat rmMisassemly/contig.*.fa >haplotypes.rm_misassembly.redundant.fa 398 | 399 | #remove redundant contigs again because some non-redundant contigs may be caused by misassembly 400 | ls rmMisassemly/contig.*.fa >contig_list.txt2 401 | python $basepath/rm_redundant_genomes.py contig_list.txt2 $max_global_divergence . $threads $max_local_divergence $maxCO 402 | cp haplotypes.fa haplotypes.rm_misassembly.fa 403 | elif [[ $rm_misassembly == "False" ]]; then 404 | cp haplotypes.fa haplotypes.rm_misassembly.fa 405 | else 406 | echo "invalid value for --rmMisassembly, should be either True or False." 407 | exit 1 408 | fi 409 | 410 | ############################################## 411 | ### Step4: low frequent haplotypes removal ### 412 | ############################################## 413 | export min_abundance=$min_abun 414 | 415 | mkdir -p filter_by_abun 416 | cd filter_by_abun || exit 417 | fa_read=../corrected.0.fa #corrected reads 418 | 419 | for i in {1..2}; 420 | do 421 | if [[ $i == 1 ]];then 422 | cat ../haplotypes.rm_misassembly.fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 423 | else 424 | cat haplotypes.final.fa | perl -ne 'BEGIN{$i=1;}if(/^>/){print ">contig_$i\n";$i+=1;}else{print;}' >haps.fa 425 | fi 426 | minimap2 -a --secondary=no -t $threads haps.fa $fa_read | samtools view -F 3584 -b -t $threads | samtools sort - >haps.bam 427 | 428 | jgi_summarize_bam_contig_depths haps.bam --percentIdentity $percent_identity --outputDepth haps.depth 429 | 430 | perl -e 'open A,"haps.depth";;my$alldepth=0;while(){my@a=split;$alldepth+=$a[2];}close A; \ 431 | open A,"haps.depth";;while(){my@a=split;my$d=$a[2]/$alldepth;print "$a[0]\t$a[1]\t$a[2]\t$d\n";}close A; ' | 432 | sort -k4nr >haps.depth.sort 433 | 434 | perl -e ' my%id2seq;$/=">";open A,"haps.fa";;while(){chomp;my@a=split;$id2seq{$a[0]}=$a[1];}close A; 435 | $/="\n"; open A,"haps.depth.sort";my$k=0;while(){chomp;my@a=split;next if $a[-1]<$ENV{"min_abundance"}; 436 | my$abun=sprintf "%.3f",$a[-1];my$cov=sprintf "%.0f",$a[-2]; $k+=1;print ">hap$k $cov"."x freq=$abun\n$id2seq{$a[0]}\n";}close A; ' >haplotypes.final.fa 437 | done 438 | #TODO, after filtering, is it necessary to recompute the abundance of haplotypes ? yes, see for loop 439 | 440 | 441 | ## Done ## 442 | cd .. 443 | ln -fs ./filter_by_abun/haplotypes.final.fa . 444 | echo 'All steps finished successfully.' 445 | echo 'The final haplotypes and their relative abundances are saved here: '$outdir'/haplotypes.final.fa' 446 | --------------------------------------------------------------------------------