├── .gitignore ├── LICENSE ├── README.md ├── pyproject.toml ├── setup.py └── src └── mobidb_lite ├── __init__.py ├── __main__.py ├── anchor.py ├── bin ├── DisEMBL │ ├── LICENSE │ ├── disembl │ └── src.zip ├── ESpritz │ ├── README │ ├── atchley │ ├── disbinD │ ├── disbinN │ ├── disbinX │ ├── model_definition │ │ ├── ensembleCX │ │ ├── ensembleCX_psi │ │ ├── ensembleD │ │ ├── ensembleD_psi │ │ ├── ensembleN │ │ ├── ensembleN_psi │ │ └── ensembleX │ ├── models │ │ ├── disprot │ │ │ ├── disprot_atchonly │ │ │ │ ├── model0 │ │ │ │ │ ├── trained--1.model │ │ │ │ │ ├── trained--10.model │ │ │ │ │ └── trained-0.model │ │ │ │ ├── model1 │ │ │ │ │ ├── trained--1.model │ │ │ │ │ ├── trained--10.model │ │ │ │ │ └── trained-0.model │ │ │ │ ├── model2 │ │ │ │ │ ├── trained--1.model │ │ │ │ │ ├── trained--10.model │ │ │ │ │ └── trained-0.model │ │ │ │ ├── model3 │ │ │ │ │ ├── trained--1.model │ │ │ │ │ ├── trained--10.model │ │ │ │ │ └── trained-0.model │ │ │ │ └── model4 │ │ │ │ │ ├── trained--1.model │ │ │ │ │ ├── trained--10.model │ │ │ │ │ └── trained-0.model │ │ │ └── disprot_seqonly │ │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ ├── nmr │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ └── xray │ │ │ ├── xray_aspritz │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── xray_sspritz │ │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ └── models_psi │ │ ├── disprot │ │ ├── disprot_aspritz │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ ├── disprot_aspritzp │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ ├── disprot_sspritz │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ └── disprot_sspritzp │ │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── nmr │ │ ├── nmr_aspritz │ │ │ ├── .disbin │ │ │ ├── .fasta │ │ │ ├── .nova │ │ │ ├── model0 │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ └── trained-0.model │ │ ├── nmr_aspritzp │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ ├── nmr_sspritz │ │ │ ├── model0 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ │ ├── trained--1.model │ │ │ │ ├── trained--10.model │ │ │ │ └── trained-0.model │ │ └── nmr_sspritzp │ │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ └── xray │ │ ├── xray_aspritz │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── xray_aspritzp │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── xray_sspritz │ │ ├── model0 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model1 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model2 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ ├── model3 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ └── model4 │ │ │ ├── trained--1.model │ │ │ ├── trained--10.model │ │ │ └── trained-0.model │ │ └── xray_sspritzp │ │ ├── model0 │ │ ├── trained--1.model │ │ ├── trained--10.model │ │ └── trained-0.model │ │ ├── model1 │ │ ├── trained--1.model │ │ ├── trained--10.model │ │ └── trained-0.model │ │ ├── model2 │ │ ├── trained--1.model │ │ ├── trained--10.model │ │ └── trained-0.model │ │ ├── model3 │ │ ├── trained--1.model │ │ ├── trained--10.model │ │ └── trained-0.model │ │ └── model4 │ │ ├── trained--1.model │ │ ├── trained--10.model │ │ └── trained-0.model ├── IUPred │ ├── LICENSE │ ├── histo │ ├── histo_casp │ ├── histo_sum │ ├── iupred_string │ ├── src.zip │ ├── ss │ └── ss_casp ├── SEG │ ├── seg │ ├── seg.doc │ └── src.zip └── TISEAN │ ├── COPYING │ ├── TISEAN_2.1.tar.gz │ └── sav_gol ├── consensus.py ├── data └── svr_model_nu_2.joblib ├── disembl.py ├── espritz.py ├── fess.py ├── globplot.py ├── iupred.py ├── nu_svr.py ├── seg.py └── tisean.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 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Many people have made 220 | generous contributions to the wide range of software distributed 221 | through that system in reliance on consistent application of that 222 | system; it is up to the author/donor to decide if he or she is willing 223 | to distribute software through any other system and a licensee cannot 224 | impose that choice. 225 | 226 | This section is intended to make thoroughly clear what is believed to 227 | be a consequence of the rest of this License. 228 | 229 | 8. If the distribution and/or use of the Program is restricted in 230 | certain countries either by patents or by copyrighted interfaces, the 231 | original copyright holder who places the Program under this License 232 | may add an explicit geographical distribution limitation excluding 233 | those countries, so that distribution is permitted only in or among 234 | countries not thus excluded. In such case, this License incorporates 235 | the limitation as if written in the body of this License. 236 | 237 | 9. The Free Software Foundation may publish revised and/or new versions 238 | of the General Public License from time to time. Such new versions will 239 | be similar in spirit to the present version, but may differ in detail to 240 | address new problems or concerns. 241 | 242 | Each version is given a distinguishing version number. If the Program 243 | specifies a version number of this License which applies to it and "any 244 | later version", you have the option of following the terms and conditions 245 | either of that version or of any later version published by the Free 246 | Software Foundation. If the Program does not specify a version number of 247 | this License, you may choose any version ever published by the Free Software 248 | Foundation. 249 | 250 | 10. If you wish to incorporate parts of the Program into other free 251 | programs whose distribution conditions are different, write to the author 252 | to ask for permission. For software which is copyrighted by the Free 253 | Software Foundation, write to the Free Software Foundation; we sometimes 254 | make exceptions for this. Our decision will be guided by the two goals 255 | of preserving the free status of all derivatives of our free software and 256 | of promoting the sharing and reuse of software generally. 257 | 258 | NO WARRANTY 259 | 260 | 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY 261 | FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN 262 | OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES 263 | PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED 264 | OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF 265 | MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS 266 | TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE 267 | PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, 268 | REPAIR OR CORRECTION. 269 | 270 | 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 271 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR 272 | REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, 273 | INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING 274 | OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED 275 | TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY 276 | YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER 277 | PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE 278 | POSSIBILITY OF SUCH DAMAGES. 279 | 280 | END OF TERMS AND CONDITIONS 281 | 282 | How to Apply These Terms to Your New Programs 283 | 284 | If you develop a new program, and you want it to be of the greatest 285 | possible use to the public, the best way to achieve this is to make it 286 | free software which everyone can redistribute and change under these terms. 287 | 288 | To do so, attach the following notices to the program. It is safest 289 | to attach them to the start of each source file to most effectively 290 | convey the exclusion of warranty; and each file should have at least 291 | the "copyright" line and a pointer to where the full notice is found. 292 | 293 | 294 | Copyright (C) 295 | 296 | This program is free software; you can redistribute it and/or modify 297 | it under the terms of the GNU General Public License as published by 298 | the Free Software Foundation; either version 2 of the License, or 299 | (at your option) any later version. 300 | 301 | This program is distributed in the hope that it will be useful, 302 | but WITHOUT ANY WARRANTY; without even the implied warranty of 303 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 304 | GNU General Public License for more details. 305 | 306 | You should have received a copy of the GNU General Public License along 307 | with this program; if not, write to the Free Software Foundation, Inc., 308 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. 309 | 310 | Also add information on how to contact you by electronic and paper mail. 311 | 312 | If the program is interactive, make it output a short notice like this 313 | when it starts in an interactive mode: 314 | 315 | Gnomovision version 69, Copyright (C) year name of author 316 | Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 317 | This is free software, and you are welcome to redistribute it 318 | under certain conditions; type `show c' for details. 319 | 320 | The hypothetical commands `show w' and `show c' should show the appropriate 321 | parts of the General Public License. Of course, the commands you use may 322 | be called something other than `show w' and `show c'; they could even be 323 | mouse-clicks or menu items--whatever suits your program. 324 | 325 | You should also get your employer (if you work as a programmer) or your 326 | school, if any, to sign a "copyright disclaimer" for the program, if 327 | necessary. Here is a sample; alter the names: 328 | 329 | Yoyodyne, Inc., hereby disclaims all copyright interest in the program 330 | `Gnomovision' (which makes passes at compilers) written by James Hacker. 331 | 332 | , 1 April 1989 333 | Ty Coon, President of Vice 334 | 335 | This General Public License does not permit incorporating your program into 336 | proprietary programs. If your program is a subroutine library, you may 337 | consider it more useful to permit linking proprietary applications with the 338 | library. If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MobiDB-lite: long disorder consensus predictor 2 | 3 | MobiDB-lite is a Python program designed to generate a consensus prediction of intrinsically disordered regions in proteins. 4 | The consensus is generated by measuring predictors agreement where at least 5/8 of predictors must agree to assign disorder state to a 5 | residue. A mathematical morphology (MM) dilation/erosion processing is applied to smooth the prediction and short regions are filtered out. 6 | 7 | The code is integrated into the [InterProScan](https://www.ebi.ac.uk/interpro/download/InterProScan/) software. 8 | The MobiDB-lite output is available in the 9 | [MobiDB](https://mobidb.org), 10 | [InterPro](https://www.ebi.ac.uk/interpro/), 11 | [PDBe](https://www.ebi.ac.uk/pdbe/), 12 | [PDBe-KB](https://www.ebi.ac.uk/pdbe/pdbe-kb/) and 13 | [UniProtKB](https://www.uniprot.org/) 14 | databases. 15 | 16 | This version of MobiDB has been developed in collaboration with Matthias Blum who optimized its execution time by 10 times. 17 | Matthias' version is available [here](https://github.com/matthiasblum/idrpred.git) (and called IDRPred). 18 | 19 | MobiDB-lite is also available as Docker container [MobiDB-lite_docker](https://github.com/BioComputingUP/MobiDB-lite_docker) 20 | 21 | ## Citation 22 | 23 | [MobiDB-lite 3.0: Fast consensus annotation of intrinsic disorder flavors in proteins](https://dx.doi.org/10.1093/bioinformatics/btaa1045) 24 | *Necci M, Piovesan D, Clementel D, Dosztányi Z, Tosatto SCE* 25 | **Bioinformatics (2020)** - DOI: [10.1093/bioinformatics/btaa1045](https://dx.doi.org/10.1093/bioinformatics/btaa1045) 26 | 27 | 28 | ## Installation 29 | You can use the tool simply cloning this repo and running `__main__.py` Python script. 30 | which implements a command line interface. 31 | To avoid import errors you need to add the source root folder 32 | to the `PYTHONPATH` environment variable. For example: 33 | 34 | export PYTHONPATH=/path/to/MobiDB-lite/src:$PYTHONPATH 35 | 36 | Alternatively, you can install the package with Pip. No need to export any variable 37 | in this case. 38 | 39 | From GitHub: 40 | 41 | git clone https://github.com/BioComputingUP/MobiDB-lite.git 42 | pip install . 43 | 44 | 45 | In the case the calculation of ensemble properties (compactness), the nu parameter, the following optional 46 | dependencies are required: 47 | 48 | * scikit-learn >= 1.4.2 49 | * numba >= 0.59.1 50 | 51 | 52 | ## Usage 53 | 54 | The program can be executing the command line interface or as a library. 55 | Both the command line and the library accept the following positional arguments: 56 | 57 | * **input file** - Protein sequences to be analyzed in FASTA format 58 | * **output file** - The output file, different formats can be generated 59 | 60 | 61 | ### Command line 62 | 63 | When executed from the command line the script logs information about the calculation in the console (standard error) and 64 | will create an output file containing the prediction results. 65 | A different temporary folder can be specified using the `--tempdir` option. 66 | 67 | The following options are available: 68 | When installed with pip the script is available as `mobidb_lite` command. 69 | 70 | ```bashcon 71 | mobidb_lite input_fasta_file output_file 72 | ``` 73 | If you simply cloned the repository: 74 | ```bashcon 75 | python3 /path/to/MobiDB-lite/src/mobidb_lite/__main__.py input_fasta_file output_file 76 | ``` -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [build-system] 2 | requires = ["setuptools"] 3 | build-backend = "setuptools.build_meta" 4 | 5 | [project] 6 | name = "MobiDB-lite" 7 | version = "4.0.0" 8 | description = "A consensus-based predictor of intrinsically disordered regions in proteins." 9 | readme = "README.md" 10 | license = {file = "LICENSE.md", 'content-type' = 'text/markdown'} 11 | authors = [ 12 | {name = "Matthias Blum", email = "mblum@ebi.ac.uk"}, 13 | {name = "Damiano Piovesan", email = "damiano.piovesan@unipd.it"}, 14 | {name = "Mahta Mehdiabadi", email = "mahta.mehdiabadi@studenti.unipd.it"} 15 | ] 16 | requires-python = ">=3.12.3" 17 | dependencies = [] 18 | 19 | [project.optional-dependencies] 20 | nu = [ 21 | "scikit-learn >= 1.4.2", 22 | "numba >= 0.59.1" 23 | ] 24 | 25 | [tool.setuptools.packages.find] 26 | where = ["src"] 27 | namespaces = false 28 | 29 | [project.scripts] 30 | mobidb_lite = "mobidb_lite.__main__:main" 31 | 32 | 33 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup() -------------------------------------------------------------------------------- /src/mobidb_lite/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/__init__.py -------------------------------------------------------------------------------- /src/mobidb_lite/__main__.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import sys 4 | from argparse import ArgumentParser, FileType 5 | from tempfile import gettempdir 6 | 7 | from mobidb_lite.consensus import run, content_count, _FEATURES, _MOBIDB_NAMES 8 | 9 | 10 | def main(): 11 | script = os.path.relpath(__file__) 12 | 13 | description = "A consensus-based predictor of intrinsically disordered regions in proteins." 14 | parser = ArgumentParser(prog=f"python {os.path.basename(script)}", description=description) 15 | parser.add_argument("infile", nargs="?", default="-", type=FileType("rt", encoding="UTF-8"), 16 | help="A file of sequences in FASTA format.") 17 | parser.add_argument("outfile", nargs="?", default="-", type=FileType("wt", encoding="UTF-8"), 18 | help="Write the output of infile to outfile.") 19 | parser.add_argument("--format", type=str, default='interpro', 20 | help=f"Output format", choices=('interpro', 'mobidb')), 21 | parser.add_argument("--force", action="store_true", default=False, 22 | help="Generate consensus as long as at least one predictor did not fail.") 23 | parser.add_argument("--extra", dest="run_extra", action="store_true", default=False, 24 | help="Run extra features (ANCHOR, ensemble properties compact/extended") 25 | parser.add_argument("--round", action="store_true", default=False, 26 | help="Round scores before threshold checks, like MobiDB-lite.") 27 | parser.add_argument("--tempdir", metavar="DIRECTORY", default=gettempdir(), 28 | help=(f"Directory to use for temporary files, " f"default: {gettempdir()}.")) 29 | parser.add_argument("--threads", type=int, default=1, 30 | help="Number of parallel threads, default: 1.") 31 | args = parser.parse_args() 32 | 33 | root = os.path.abspath(os.path.dirname(script)) 34 | bindir = os.path.join(root, "bin") 35 | datadir = os.path.join(root, "data") 36 | 37 | with args.outfile as outfile: 38 | for seq_id, regions, scores in run(args.infile, bindir, datadir, args.threads, 39 | force=args.force, 40 | round=args.round, 41 | extra=args.run_extra, 42 | tempdir=args.tempdir): 43 | if regions is None: 44 | sys.stderr.write(f"error in {seq_id}\n") 45 | continue 46 | 47 | seq_len = len(list(scores.values())[0]) # A random method 48 | 49 | if args.format == "interpro": 50 | for feature, region in regions.items(): 51 | if feature in _FEATURES: 52 | for start, end in region: 53 | outfile.write(f"{seq_id}\t{start}\t{end}\t{feature}\n") 54 | elif feature == "mobidblite": 55 | for start, end in region: 56 | outfile.write(f"{seq_id}\t{start}\t{end}\t-\n") 57 | 58 | elif args.format == "mobidb": 59 | obj = {"acc": seq_id, "length": seq_len} 60 | # print(seq_id) 61 | for feature, region in regions.items(): 62 | cont_count = content_count(region) 63 | cont_fraction = round(cont_count / seq_len, 3) 64 | obj[_MOBIDB_NAMES[feature]] = {"regions": [r[:2] for r in region], 65 | "content_count": cont_count, 66 | "content_fraction": cont_fraction} 67 | if feature == "mobidblite": 68 | obj[_MOBIDB_NAMES[feature]]["scores"] = scores[feature] 69 | if feature in ["Compact", "Extended"]: 70 | obj[_MOBIDB_NAMES[feature]]["regions_params"] = [round(r[2], 3) for r in region] 71 | outfile.write(json.dumps(obj) + "\n") 72 | 73 | 74 | if __name__ == "__main__": 75 | main() -------------------------------------------------------------------------------- /src/mobidb_lite/anchor.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def run_anchor(bindir: str, file: str): 6 | cmd = [ 7 | os.path.join(bindir, "anchor"), 8 | file 9 | ] 10 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, env={"ANCHOR_PATH": bindir}, 11 | encoding="utf-8") 12 | out, err = proc.communicate() 13 | 14 | if proc.returncode == 0: 15 | scores = [] 16 | for line in out.splitlines(): 17 | if line and line[0] != "#": 18 | pos, aa, score, state = line.split() 19 | scores.append(float(score)) 20 | 21 | return scores 22 | 23 | return None 24 | 25 | 26 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/DisEMBL/LICENSE: -------------------------------------------------------------------------------- 1 | The GNU General Public License (GPL) 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc. 5 | 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA 6 | 7 | Everyone is permitted to copy and distribute verbatim copies 8 | of this license document, but changing it is not allowed. 9 | 10 | Preamble 11 | 12 | The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. 13 | When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. 14 | To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. 15 | For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. 16 | We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. 17 | Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. 18 | Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. 19 | The precise terms and conditions for copying, distribution and modification follow. 20 | 21 | TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 22 | 23 | 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". 24 | Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 25 | 26 | 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. 27 | You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 28 | 29 | 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: 30 | 31 | a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. 32 | b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. 33 | c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. 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But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. 36 | Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. 37 | In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 38 | 39 | 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: 40 | 41 | a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, 42 | b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, 43 | c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) 44 | 45 | The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. 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However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 49 | 50 | 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 51 | 52 | 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 53 | 54 | 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. 55 | If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. 56 | 57 | It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. 58 | This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 59 | 60 | 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 61 | 62 | 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. 63 | 64 | Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 65 | 66 | 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. 67 | 68 | NO WARRANTY 69 | 70 | 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 71 | 72 | 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 73 | 74 | END OF TERMS AND CONDITIONS 75 | 76 | How to Apply These Terms to Your New Programs 77 | 78 | If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. 79 | To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. 80 | one line to give the program's name and a brief idea of what it does. 81 | Copyright (C) 82 | 83 | This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. 84 | This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. 85 | You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA 86 | 87 | Also add information on how to contact you by electronic and paper mail. 88 | 89 | If the program is interactive, make it output a short notice like this when it starts in an interactive mode: 90 | 91 | Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. 92 | 93 | The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. 94 | 95 | You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: 96 | 97 | Yoyodyne, Inc., hereby disclaims all copyright interest 98 | in the program `Gnomovision' (which makes passes at compilers) 99 | written by James Hacker. 100 | 101 | signature of Ty Coon, 1 April 1989 102 | Ty Coon, President of Vice 103 | 104 | This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. 105 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/DisEMBL/disembl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/DisEMBL/disembl -------------------------------------------------------------------------------- /src/mobidb_lite/bin/DisEMBL/src.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/DisEMBL/src.zip -------------------------------------------------------------------------------- /src/mobidb_lite/bin/ESpritz/README: -------------------------------------------------------------------------------- 1 | Efficient Spritz (ESpritz) 2 | -------------------------------------------------------------- 3 | (c) Ian Walsh and Silvio C.E. Tosatto, 2011. 4 | E-mail: 5 | -------------------------------------------------------------- 6 | README File 7 | Version 1.1 8 | 9 | Espritz - Efficient prediciton of protein disorder segments 10 | -------------------------------------------------------------- 11 | 12 | 1. Introduction 13 | Espritz is software to detect regions of proteins which are thought to contain no structural content. This non-structure 14 | is known as protein disorder. Espritz uses an efficient and accurate prediction algorithm based on Bi-directional 15 | Recursive Neural networks (BRNN's). BRNN's are a sequence based machine learning algorithm which have being found to be useful 16 | for other structural predictions such as secondary structure. The method is based solely on the sequence and does not use 17 | expensive computations to find multiple sequence alignments. 18 | For a slightly more detailed technical description of the capabilities and technology used, see the methods page on our server 19 | (http://protein.bio.unipd.it/espritz/). For the most detailed description see our "I. Walsh, A.J.M Martin and S. Tosatto. 20 | Sequence is sufficient for accurate disorder predictions applied to various genomes, Bioinformatics, SUBMITTED, 2011". 21 | 22 | INSTALL 23 | 24 | ***** This software has been tested only on linux based systems. Perl, C++ runtime must be installed on your system 25 | ***** This software may be installed on MAC OS and Windows but we have no support at the moment for these operating systems. 26 | 27 | 28 | 29 | The sequence based fast predictors should work without any tweaking. This is because all files are contained within your folder. 30 | 31 | 32 | 33 | However for psi-blast based predictions we recommend the following procedure: 34 | 35 | (1) Download the latest BLAST binaries at ftp://ftp.ncbi.nlm.nih.gov/blast/executables/release/LATEST/. We 36 | used blast-2.2.25-ia32-linux.tar.gz (36MB) for our linux distribution. 37 | (2) Deflate the compressed file: 38 | tar -xzf blast-2.2.25-ia32-linux.tar.gz 39 | 40 | (3) In your new installation record the path to the blastlocation/blast-2.2.25/bin/ directory where blastlocation 41 | is the directory where you decompressed the blast-2.2.25-ia32-linux.tar.gz file. 42 | 43 | (4) Edit the ./align/getAlignments.pl perl script and replace the $blastdir variable with your new 44 | path blastlocation/blast-2.2.25/bin/ 45 | 46 | (4) Next we need to download the latest blast databases. Conveniently the newest blast distribution above 47 | contains a script to update your sequence databases. Execute the following: 48 | cd blastlocation/ncbi-blast-2.2.25+/ 49 | mkdir blastdb 50 | download nr.gz at ftp://ftp.ncbi.nih.gov/blast/db/FASTA/ 51 | 52 | Decompress the latest nr sequence database: 53 | gzip -d nr.gz 54 | 55 | (5) The sequences contain a lot of redundancy. Filter the sequences with the "cd-hit" method http://www.bioinformatics.org/cd-hit/. 56 | Once it is downloaded decompress it: 57 | cd location_of_cd-hit 58 | tar -xzf cd-hit* 59 | make 60 | sudo make install (or do "make install" as administrator) 61 | cdhit -c 0.9 -i nr -o nr90 62 | 63 | (6) Our tests have shown that filtering the fasta sequences downloaded in (4) with masked low complexity regions were useful 64 | for our disorder predictors. For this we used the pfilt agorithm http://bioinfadmin.cs.ucl.ac.uk/downloads/pfilt/. Follow the instructions 65 | at this site to compile pfilt. Then execute: 66 | ./pfilt nr90 > nr90filt 67 | The resulting nr90filt are the final sequences used for calculations of alignments. Alignments generated in this manner were found to 68 | be optimal for disorder prediction. 69 | 70 | (7) Format the nr90filt into the blastdb format: 71 | blastlocation/blast-2.2.25/bin/formatdb -p T -i nr90filt 72 | 73 | (8) Finally, create a file called .ncbirc on your home directory (e.g. /home/user/) and add: 74 | [NCBI] 75 | data=/home/user/blast/data 76 | 77 | [BLAST] 78 | BLASTDB=/home/user/blast/db 79 | BLASTMAT=/home/user/blast/data 80 | Save and start using ESpritz in a new terminal. 81 | 82 | Notes: 83 | * (1) and (4) may be time consuming depending on your internet connection. Moreover, several gigabytes of hard disk space will be used. 84 | 85 | * The above psi-blast setup is only a recommendation. Your favorite method can be used. However, be careful that 86 | the script ./align/getAlignments.pl uses the "psiblast" binary which replaces the old "blastpgp" binary. 87 | 88 | * We recommend not to use psi-blast unless you want the extra 1-2% accuracy. We believe the advatages of just using the sequence based predictors outway 89 | the advantages for using ESpritz with psi-blast. 90 | 91 | 92 | 93 | Input Options 94 | Working directory 95 | This is the directory which contains all the fasta files which ESpritz will predict. IMPORTANT: The files must have the .fasta extension. 96 | See EXAMPLES.sh to see how to run the software on some example fasta sequences. 97 | 98 | x-ray, Disprot or NMR disorder 99 | * x-ray: This is based on missing atoms from the Protein Data Bank (PDB) X-ray solved structures. If this option is 100 | chosen then the predictors with short x-ray disorder options are executed. See EXAMPLES.sh to see how to select this option. 101 | * disprot: The dataset used for this definition contains long disorder segments. In particular, disprot a manually curated 102 | database which is often based on functional attributes of the disordered region. If this option is chosen then the predictors 103 | with long disorder options are executed. See EXAMPLES.sh to see how to select this option. 104 | * nmr: The dataset used for this definition contains was defined using our server MOBI (http://protein.bio.unipd.it/mobi). 105 | This disorder is basedon the flexibility/mobility between different models in NMR structures. See EXAMPLES.sh to see how to select this option. 106 | 107 | 108 | Probability decision 109 | * Best Sw: The definition of Sw can be found in the CASP assessments. This option chooses the threshold which maximised the 110 | Sw measure on the training set. This threshold tends to over-predict disorder but is nonetheless useful. See EXAMPLES.sh to 111 | see how to select this option. 112 | * 5% False Positive Rate (FPR): This is a stricter definition of disorder compared to best Sw. It is the threshold which 113 | produces 5% False positives on the training data. See EXAMPLES.sh to see how to select this option. 114 | 115 | 116 | Output 117 | After prediction all the fasta files in the working directory will have its corresponding prediction file (.espritz extension). These prediction 118 | files will also be placed in the working directory choosen at input. 119 | 120 | 121 | 122 | References: 123 | 124 | Please cite this if you use our software): 125 | Espritz bla bla 126 | 127 | psi-blast 128 | Altschul, S.F., et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of 129 | protein database search programs, Nucleic Acids Res, 25, 3389-3402. 130 | 131 | 132 | cd-hit 133 | "Clustering of highly homologous sequences to reduce the size of large protein database", Weizhong Li, Lukasz 134 | Jaroszewski & Adam Godzik Bioinformatics, (2001) 17:282-283 135 | 136 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/ESpritz/atchley: -------------------------------------------------------------------------------- 1 | -0.591 -1.302 -0.733 1.570 -0.146 2 | -1.343 0.465 -0.862 -1.020 -0.255 3 | 1.050 0.302 -3.656 -0.259 -3.242 4 | 1.357 -1.453 1.477 0.113 -0.837 5 | -1.006 -0.590 1.891 -0.397 0.412 6 | -0.384 1.652 1.330 1.045 2.064 7 | 0.336 -0.417 -1.673 -1.474 -0.078 8 | -1.239 -0.547 2.131 0.393 0.816 9 | 1.831 -0.561 0.533 -0.277 1.648 10 | -1.019 -0.987 -1.505 1.266 -0.912 11 | -0.663 -1.524 2.219 -1.005 1.212 12 | 0.945 0.828 1.299 -0.169 0.933 13 | 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-------------------------------------------------------------------------------- 1 | 20 2 | ./models_psi/xray/xray_sspritz/model0/trained-0.model 3 | ./models_psi/xray/xray_aspritz/model0/trained-0.model 4 | ./models_psi/xray/xray_sspritz/model1/trained-0.model 5 | ./models_psi/xray/xray_aspritz/model1/trained-0.model 6 | ./models_psi/xray/xray_sspritz/model2/trained-0.model 7 | ./models_psi/xray/xray_aspritz/model2/trained-0.model 8 | ./models_psi/xray/xray_sspritz/model3/trained-0.model 9 | ./models_psi/xray/xray_aspritz/model3/trained-0.model 10 | ./models_psi/xray/xray_sspritz/model4/trained-0.model 11 | ./models_psi/xray/xray_aspritz/model4/trained-0.model 12 | ./models_psi/xray/xray_sspritzp/model0/trained-0.model 13 | ./models_psi/xray/xray_aspritzp/model0/trained-0.model 14 | ./models_psi/xray/xray_sspritzp/model1/trained-0.model 15 | ./models_psi/xray/xray_aspritzp/model1/trained-0.model 16 | ./models_psi/xray/xray_sspritzp/model2/trained-0.model 17 | ./models_psi/xray/xray_aspritzp/model2/trained-0.model 18 | ./models_psi/xray/xray_sspritzp/model3/trained-0.model 19 | ./models_psi/xray/xray_aspritzp/model3/trained-0.model 20 | ./models_psi/xray/xray_sspritzp/model4/trained-0.model 21 | ./models_psi/xray/xray_aspritzp/model4/trained-0.model 22 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/ESpritz/model_definition/ensembleD: -------------------------------------------------------------------------------- 1 | 10 2 | ./models/disprot/disprot_seqonly/model0/trained-0.model 3 | ./models/disprot/disprot_seqonly/model1/trained-0.model 4 | ./models/disprot/disprot_seqonly/model2/trained-0.model 5 | ./models/disprot/disprot_seqonly/model3/trained-0.model 6 | ./models/disprot/disprot_seqonly/model4/trained-0.model 7 | ./models/disprot/disprot_atchonly/model0/trained-0.model 8 | ./models/disprot/disprot_atchonly/model1/trained-0.model 9 | 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nontransferable right to use the IUPred executable, source 6 | code and related documentation ("Licensed Material") is granted 7 | by Zsuzsanna Dosztanyi ("Author") to 8 | 9 | 10 | .................................... 11 | 12 | in exchange of the following commitments: 13 | 14 | I hereby certify and accept that 15 | 16 | 1. I am an academic user at an academic research institution. In using the 17 | Licensed Material, I will respect the interests of the Author and the 18 | Institute of Enzymology. 19 | 20 | 2. All rights, title, and interest in and to the Licensed Material shall at 21 | all times remain with the Author. 22 | 23 | 3. I may, for internal use only, execute, enhance, modify, or expand the 24 | Licensed Material, may merge it into other material, and may use the 25 | Licensed Material as modified. All such modifications shall include the 26 | Author copyright notice. 27 | 28 | 4. There is no warranty or other guarantee of fitness of the Licensed 29 | Material. It is provided solely "as is". The Author disclaims all res- 30 | ponsibility and liability with respect to this software's usage or its 31 | effect upon hardware or computer systems. The Author has no obligation 32 | to provide support or maintenance services for the Licensed Material. 33 | 34 | 5. I will not use the software in commercial activities. Incorporation of 35 | the Software into a commercial Web site or other fee paying service is 36 | not allowed. 37 | 38 | 6. The program shall not be made available to users outside the recipient's 39 | laboratory. I will suggest to other interested research groups to 40 | contact the authors directly. 41 | 42 | 7. I will not alter or suppress the run-time copyright message. 43 | 44 | 8. I will acknowledge the program authors on any publication of scientific 45 | results based in part on use of the program and cite the article in 46 | which the program was described: 47 | 48 | The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates 49 | between Folded and Intrinsically Unstructured Proteins 50 | Zsuzsanna Dosztanyi, Veronika Csizmok, Peter Tompa and Istvan Simon 51 | J. Mol. Biol. (2005) 347, 827-839. 52 | 53 | 9. I will report evidence of program bugs to the Author. 54 | 55 | Purpose of research: 56 | 57 | 58 | Full name and institutional address: 59 | 60 | 61 | 62 | Internet email address: 63 | 64 | Date: Signature: 65 | 66 | 67 | 68 | 69 | Mail of fax the signed form to: 70 | 71 | Dr Zsuzsanna Dosztanyi 72 | Institute of Enzymology 73 | Biological Research Center 74 | Hungarian Academy of Sciences 75 | H-1518 Budapest PO Box 7 76 | Fax: +36-1 466-5465 77 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/IUPred/iupred_string: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/IUPred/iupred_string -------------------------------------------------------------------------------- /src/mobidb_lite/bin/IUPred/src.zip: -------------------------------------------------------------------------------- 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5 F 16 E 1.29915607 118 | 5 F 17 K -0.46606451 119 | 5 F 18 R -0.81115817 120 | 5 F 19 H -2.44199924 121 | 6 P 0 G 1.12692286 122 | 6 P 1 A 1.01113262 123 | 6 P 2 V -0.02286964 124 | 6 P 3 L 1.54727916 125 | 6 P 4 I 1.48929903 126 | 6 P 5 F 1.31624529 127 | 6 P 6 P -0.48638854 128 | 6 P 7 S 0.68652014 129 | 6 P 8 T 1.17119738 130 | 6 P 9 C -0.35518570 131 | 6 P 10 M -0.09194104 132 | 6 P 11 W -1.31777091 133 | 6 P 12 Y -0.01450234 134 | 6 P 13 N 0.51822425 135 | 6 P 14 Q 2.62319390 136 | 6 P 15 D 2.31694228 137 | 6 P 16 E 1.01822603 138 | 6 P 17 K 0.87824055 139 | 6 P 18 R 1.03582756 140 | 6 P 19 H 0.62569242 141 | 7 S 0 G 0.58072784 142 | 7 S 1 A -0.04041028 143 | 7 S 2 V 0.12179873 144 | 7 S 3 L 0.32185690 145 | 7 S 4 I 0.37047801 146 | 7 S 5 F -1.02681044 147 | 7 S 6 P 0.68652014 148 | 7 S 7 S -0.36011304 149 | 7 S 8 T 0.05196303 150 | 7 S 9 C 0.41553225 151 | 7 S 10 M 1.02132771 152 | 7 S 11 W -1.40421985 153 | 7 S 12 Y -0.57818834 154 | 7 S 13 N 0.41117586 155 | 7 S 14 Q 1.07734870 156 | 7 S 15 D 0.66588112 157 | 7 S 16 E 0.63290613 158 | 7 S 17 K 0.21568839 159 | 7 S 18 R 0.79375027 160 | 7 S 19 H 0.80499846 161 | 8 T 0 G 0.48489851 162 | 8 T 1 A 0.25537039 163 | 8 T 2 V -0.12056881 164 | 8 T 3 L 0.46440627 165 | 8 T 4 I 0.03594495 166 | 8 T 5 F -0.14088095 167 | 8 T 6 P 1.17119738 168 | 8 T 7 S 0.05196303 169 | 8 T 8 T -0.18198720 170 | 8 T 9 C -1.45158011 171 | 8 T 10 M 0.73718850 172 | 8 T 11 W 0.44017078 173 | 8 T 12 Y -0.60410153 174 | 8 T 13 N 0.27935055 175 | 8 T 14 Q -0.19148574 176 | 8 T 15 D -0.52535654 177 | 8 T 16 E 1.08693107 178 | 8 T 17 K -0.53468577 179 | 8 T 18 R 1.21292504 180 | 8 T 19 H 0.26637175 181 | 9 C 0 G -2.21534292 182 | 9 C 1 A -2.33015437 183 | 9 C 2 V -1.03314224 184 | 9 C 3 L -1.88528760 185 | 9 C 4 I 0.50915205 186 | 9 C 5 F -3.22136449 187 | 9 C 6 P -0.35518570 188 | 9 C 7 S 0.41553225 189 | 9 C 8 T -1.45158011 190 | 9 C 9 C -27.93919270 191 | 9 C 10 M -0.10450540 192 | 9 C 11 W 2.23000662 193 | 9 C 12 Y -2.46428628 194 | 9 C 13 N -2.02097564 195 | 9 C 14 Q -2.70514014 196 | 9 C 15 D -0.34352913 197 | 9 C 16 E -0.07177225 198 | 9 C 17 K -0.82317443 199 | 9 C 18 R -0.33193859 200 | 9 C 19 H -5.38606607 201 | 10 M 0 G -0.06215472 202 | 10 M 1 A -1.34107975 203 | 10 M 2 V -1.80144194 204 | 10 M 3 L -1.80808171 205 | 10 M 4 I -1.66151250 206 | 10 M 5 F -3.87839804 207 | 10 M 6 P -0.09194104 208 | 10 M 7 S 1.02132771 209 | 10 M 8 T 0.73718850 210 | 10 M 9 C -0.10450540 211 | 10 M 10 M -1.57090043 212 | 10 M 11 W -3.23582737 213 | 10 M 12 Y -5.19968257 214 | 10 M 13 N 0.21432961 215 | 10 M 14 Q 0.58241128 216 | 10 M 15 D 0.42546691 217 | 10 M 16 E 0.93713430 218 | 10 M 17 K 1.36937658 219 | 10 M 18 R 1.26392745 220 | 10 M 19 H -0.58833592 221 | 11 W 0 G 1.08948886 222 | 11 W 1 A 0.35470666 223 | 11 W 2 V -1.93411798 224 | 11 W 3 L -4.70049024 225 | 11 W 4 I -2.18705039 226 | 11 W 5 F -3.11777737 227 | 11 W 6 P -1.31777091 228 | 11 W 7 S -1.40421985 229 | 11 W 8 T 0.44017078 230 | 11 W 9 C 2.23000662 231 | 11 W 10 M -3.23582737 232 | 11 W 11 W 1.14233191 233 | 11 W 12 Y -8.26989925 234 | 11 W 13 N -0.67827127 235 | 11 W 14 Q -0.38454710 236 | 11 W 15 D -1.03915767 237 | 11 W 16 E -0.63589363 238 | 11 W 17 K -0.16299379 239 | 11 W 18 R -4.28949847 240 | 11 W 19 H -5.90888390 241 | 12 Y 0 G -1.46825645 242 | 12 Y 1 A -2.61928356 243 | 12 Y 2 V -2.52904874 244 | 12 Y 3 L -3.50131333 245 | 12 Y 4 I -4.12253411 246 | 12 Y 5 F -4.64434088 247 | 12 Y 6 P -0.01450234 248 | 12 Y 7 S -0.57818834 249 | 12 Y 8 T -0.60410153 250 | 12 Y 9 C -2.46428628 251 | 12 Y 10 M -5.19968257 252 | 12 Y 11 W -8.26989925 253 | 12 Y 12 Y -2.44310630 254 | 12 Y 13 N 0.36478085 255 | 12 Y 14 Q 0.07882995 256 | 12 Y 15 D 1.05167795 257 | 12 Y 16 E 0.33777377 258 | 12 Y 17 K -0.43594232 259 | 12 Y 18 R 0.10345161 260 | 12 Y 19 H -1.61656350 261 | 13 N 0 G -0.17280222 262 | 13 N 1 A 0.27020631 263 | 13 N 2 V 0.68029152 264 | 13 N 3 L 0.30666970 265 | 13 N 4 I -0.93304505 266 | 13 N 5 F -0.05614576 267 | 13 N 6 P 0.51822425 268 | 13 N 7 S 0.41117586 269 | 13 N 8 T 0.27935055 270 | 13 N 9 C -2.02097564 271 | 13 N 10 M 0.21432961 272 | 13 N 11 W -0.67827127 273 | 13 N 12 Y 0.36478085 274 | 13 N 13 N 0.62097997 275 | 13 N 14 Q 1.35901649 276 | 13 N 15 D 0.25540568 277 | 13 N 16 E 0.09633481 278 | 13 N 17 K 0.99776471 279 | 13 N 18 R 0.81833846 280 | 13 N 19 H 0.76335947 281 | 14 Q 0 G 1.21558991 282 | 14 Q 1 A 0.29020380 283 | 14 Q 2 V -1.01303044 284 | 14 Q 3 L -0.32809736 285 | 14 Q 4 I -0.52837262 286 | 14 Q 5 F 0.87607973 287 | 14 Q 6 P 2.62319390 288 | 14 Q 7 S 1.07734870 289 | 14 Q 8 T -0.19148574 290 | 14 Q 9 C -2.70514014 291 | 14 Q 10 M 0.58241128 292 | 14 Q 11 W -0.38454710 293 | 14 Q 12 Y 0.07882995 294 | 14 Q 13 N 1.35901649 295 | 14 Q 14 Q -0.73904995 296 | 14 Q 15 D 1.89317894 297 | 14 Q 16 E 0.48143781 298 | 14 Q 17 K 0.52715769 299 | 14 Q 18 R 1.19396069 300 | 14 Q 19 H 2.09997851 301 | 15 D 0 G 0.80605606 302 | 15 D 1 A 0.77209498 303 | 15 D 2 V 0.99711578 304 | 15 D 3 L 0.16183350 305 | 15 D 4 I 0.59684109 306 | 15 D 5 F 0.24482101 307 | 15 D 6 P 2.31694228 308 | 15 D 7 S 0.66588112 309 | 15 D 8 T -0.52535654 310 | 15 D 9 C -0.34352913 311 | 15 D 10 M 0.42546691 312 | 15 D 11 W -1.03915767 313 | 15 D 12 Y 1.05167795 314 | 15 D 13 N 0.25540568 315 | 15 D 14 Q 1.89317894 316 | 15 D 15 D 0.60034147 317 | 15 D 16 E 1.85139995 318 | 15 D 17 K -1.36734667 319 | 15 D 18 R -1.30876437 320 | 15 D 19 H -0.95542218 321 | 16 E 0 G 1.13025024 322 | 16 E 1 A 1.17685040 323 | 16 E 2 V 0.27600132 324 | 16 E 3 L 0.87569996 325 | 16 E 4 I 0.63662246 326 | 16 E 5 F 1.29915607 327 | 16 E 6 P 1.01822603 328 | 16 E 7 S 0.63290613 329 | 16 E 8 T 1.08693107 330 | 16 E 9 C -0.07177225 331 | 16 E 10 M 0.93713430 332 | 16 E 11 W -0.63589363 333 | 16 E 12 Y 0.33777377 334 | 16 E 13 N 0.09633481 335 | 16 E 14 Q 0.48143781 336 | 16 E 15 D 1.85139995 337 | 16 E 16 E 1.40569245 338 | 16 E 17 K -1.92611469 339 | 16 E 18 R -2.27559443 340 | 16 E 19 H 0.44068656 341 | 17 K 0 G 0.13027794 342 | 17 K 1 A 0.27703267 343 | 17 K 2 V -0.07710320 344 | 17 K 3 L 0.47012503 345 | 17 K 4 I 0.01917608 346 | 17 K 5 F -0.46606451 347 | 17 K 6 P 0.87824055 348 | 17 K 7 S 0.21568839 349 | 17 K 8 T -0.53468577 350 | 17 K 9 C -0.82317443 351 | 17 K 10 M 1.36937658 352 | 17 K 11 W -0.16299379 353 | 17 K 12 Y -0.43594232 354 | 17 K 13 N 0.99776471 355 | 17 K 14 Q 0.52715769 356 | 17 K 15 D -1.36734667 357 | 17 K 16 E -1.92611469 358 | 17 K 17 K 1.27109387 359 | 17 K 18 R 2.11666999 360 | 17 K 19 H 2.29354688 361 | 18 R 0 G 0.90623557 362 | 18 R 1 A 0.85021332 363 | 18 R 2 V -0.31378236 364 | 18 R 3 L -0.20153959 365 | 18 R 4 I -0.30679850 366 | 18 R 5 F -0.81115817 367 | 18 R 6 P 1.03582756 368 | 18 R 7 S 0.79375027 369 | 18 R 8 T 1.21292504 370 | 18 R 9 C -0.33193859 371 | 18 R 10 M 1.26392745 372 | 18 R 11 W -4.28949847 373 | 18 R 12 Y 0.10345161 374 | 18 R 13 N 0.81833846 375 | 18 R 14 Q 1.19396069 376 | 18 R 15 D -1.30876437 377 | 18 R 16 E -2.27559443 378 | 18 R 17 K 2.11666999 379 | 18 R 18 R 0.43397109 380 | 18 R 19 H 1.04173970 381 | 19 H 0 G 0.65396747 382 | 19 H 1 A 1.38618902 383 | 19 H 2 V 0.77864744 384 | 19 H 3 L -0.87394415 385 | 19 H 4 I 0.10409806 386 | 19 H 5 F -2.44199924 387 | 19 H 6 P 0.62569242 388 | 19 H 7 S 0.80499846 389 | 19 H 8 T 0.26637175 390 | 19 H 9 C -5.38606607 391 | 19 H 10 M -0.58833592 392 | 19 H 11 W -5.90888390 393 | 19 H 12 Y -1.61656350 394 | 19 H 13 N 0.76335947 395 | 19 H 14 Q 2.09997851 396 | 19 H 15 D -0.95542218 397 | 19 H 16 E 0.44068656 398 | 19 H 17 K 2.29354688 399 | 19 H 18 R 1.04173970 400 | 19 H 19 H 1.32497365 401 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/SEG/seg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/SEG/seg -------------------------------------------------------------------------------- /src/mobidb_lite/bin/SEG/seg.doc: -------------------------------------------------------------------------------- 1 | 2 | DOCUMENTATION OF SEG (FROM 'MAN' PAGE) 3 | -------------------------------------- 4 | 5 | 6 | NAME 7 | ---- 8 | seg - segment sequence(s) by local complexity 9 | 10 | 11 | SYNOPSIS 12 | -------- 13 | seg sequence [ W ] [ K(1) ] [ K(2) ] [ -x ] [ options ] 14 | 15 | 16 | DESCRIPTION 17 | ----------- 18 | 19 | seg divides sequences into contrasting segments of low-complexity 20 | and high-complexity. Low-complexity segments defined by the 21 | algorithm represent "simple sequences" or "compositionally-biased 22 | regions". 23 | 24 | Locally-optimized low-complexity segments are produced at defined 25 | levels of stringency, based on formal definitions of local 26 | compositional complexity (Wootton & Federhen, 1993). The segment 27 | lengths and the number of segments per sequence are determined 28 | automatically by the algorithm. 29 | 30 | The input is a FASTA-formatted sequence file, or a database file 31 | containing many FASTA-formatted sequences. seg is tuned for amino 32 | acid sequences. For nucleotide sequences, see EXAMPLES OF 33 | PARAMETER SETS below. 34 | 35 | The stringency of the search for low-complexity segments is 36 | determined by three user-defined parameters, trigger window length 37 | [ W ], trigger complexity [ K(1) ] and extension complexity [ K(2)] 38 | (see below under PARAMETERS ). The defaults provided are suitable 39 | for low-complexity masking of database search query sequences [ -x 40 | option required, see below]. 41 | 42 | 43 | OUTPUTS AND APPLICATIONS 44 | ------------------------ 45 | 46 | (1) Readable segmented sequence [Default]. Regions of contrasting 47 | complexity are displayed in "tree format". See EXAMPLES. 48 | 49 | (2) Low-complexity masking (see Altschul et al, 1994). Produce a 50 | masked FASTA-formatted file, ready for input as a query sequence for 51 | database search programs such as BLAST or FASTA. The amino acids in 52 | low-complexity regions are replaced with "x" characters [-x option]. 53 | See EXAMPLES. 54 | 55 | (3) Database construction. Produce FASTA-formatted files containing 56 | low-complexity segments [-l option], or high-complexity segments 57 | [-h option], or both [-a option]. Each segment is a separate 58 | sequence entry with an informative header line. 59 | 60 | 61 | ALGORITHM 62 | --------- 63 | 64 | The SEG algorithm has two stages. First, identification of 65 | approximate raw segments of low- complexity; second local 66 | optimization. 67 | 68 | At the first stage, the stringency and resolution of the search for 69 | low-complexity segments is determined by the W, K(1) and K(2) 70 | parameters. All trigger windows are defined, including overlapping 71 | windows, of length W and complexity less than or equal to K(1). 72 | "Complexity" here is defined by equation (3) of Wootton & Federhen 73 | (1993). Each trigger window is then extended into a contig in both 74 | directions by merging with extension windows, which are overlapping 75 | windows of length W and complexity less than or equal to K(2). 76 | Each contig is a raw segment. 77 | 78 | At the second stage, each raw segment is reduced to a single 79 | optimal low-complexity segment, which may be the entire raw 80 | segment but is usually a subsequence. The optimal subsequence has 81 | the lowest value of the probability P(0) (equation (5) of Wootton 82 | & Federhen, 1993). 83 | 84 | PARAMETERS 85 | ---------- 86 | 87 | These three numeric parameters are in obligatory order after the 88 | sequence file name. 89 | 90 | Trigger window length [ W ]. An integer greater than zero [ Default 91 | 12 ]. 92 | 93 | Trigger complexity. [ K1 ]. The maximum complexity of a trigger 94 | window in units of bits. K1 must be equal to or greater than zero. 95 | The maximum value is 4.322 (log[base 2]20) for amino acid 96 | sequences [ Default 2.2 ]. 97 | 98 | Extension complexity [ K2 ]. The maximum complexity of an extension 99 | window in units of bits. Only values greater than K1 are effective 100 | in extending triggered windows. Range of possible values is as for 101 | K1 [ Default 2.5 ]. 102 | 103 | 104 | OPTIONS 105 | ------- 106 | 107 | The following options may be placed in any order in the command 108 | line after the W, K1 and K2 parameters: 109 | 110 | -a Output both low-complexity and high-complexity segments in a 111 | FASTA-formatted file, as a set of separate entries with header 112 | lines. 113 | 114 | -c [characters-per-line] Number of sequence characters per line of 115 | output [Default 60]. Other characters, such as residue numbers, 116 | are additional. 117 | 118 | -h Output only the high-complexity segments in a FASTA-formatted 119 | file, as a set of separate entries with header lines. 120 | 121 | -l Output only the low-complexity segments in a FASTA-formatted 122 | file, as a set of separate entries with header lines. 123 | 124 | -m [length] Minimum length in residues for a high-complexity 125 | segment [default 0]. Shorter segments are merged with adjacent 126 | low-complexity segments. 127 | 128 | -o Show all overlapping, independently-triggered low-complexity 129 | segments [these are merged by default]. 130 | 131 | -q Produce an output format with the sequence in a numbered block 132 | with markings to assist residue counting. The low-complexity and 133 | high-complexity segments are in lower- and upper-case characters 134 | respectively. 135 | 136 | -t [length] "Maximum trim length" parameter [default 100]. This 137 | controls the search space (and search time) during the 138 | optimization of raw segments (see ALGORITHM above). By default, 139 | subsequences 100 or more residues shorter than the raw segment are 140 | omitted from the search. This parameter may be increased to give 141 | a more extensive search if raw segments are longer than 100 residues. 142 | 143 | -x The masking option for amino acid sequences. Each input 144 | sequence is represented by a single output sequence in FASTA-format 145 | with low-complexity regions replaced by strings of "x" characters. 146 | 147 | 148 | EXAMPLES OF PARAMETER SETS 149 | -------------------------- 150 | 151 | Default parameters are given by 'seg sequence' (equivalent to 'seg 152 | sequence 12 2.2 2.5'). These parameters are appropriate for low- 153 | complexity masking of many amino acid sequences [with -x option ]. 154 | 155 | Database-database comparisons: 156 | ----------------------------- 157 | More stringent (lower) complexity parameters are suitable when 158 | masked sequences are compared with masked sequences. For example, 159 | for BLAST or FASTA searches that compare two amino acid sequence 160 | databases, the following masking may be applied to both databases: 161 | 162 | seg database 12 1.8 2.0 -x 163 | 164 | Homopolymer analysis: 165 | -------------------- 166 | To examine all homopolymeric subsequences of length (for example) 167 | 7 or greater: 168 | 169 | seg sequence 7 0 0 170 | 171 | Non-globular regions of protein sequences: 172 | ----------------------------------------- 173 | Many long non-globular domains may be diagnosed at longer window 174 | lengths, typically: 175 | 176 | seg sequence 45 3.4 3.75 177 | 178 | For some shorter non-globular domains, the following set is 179 | appropriate: 180 | 181 | seg sequence 25 3.0 3.3 182 | 183 | Nucleotide sequences: 184 | -------------------- 185 | The maximum value of the complexity parameters is 2 (log[base 2]4). 186 | For masking, the following is approximately equivalent in effect 187 | to the default parameters for amino acid sequences: 188 | 189 | seg sequence.na 21 1.4 1.6 190 | 191 | EXAMPLES 192 | The following is a file named 'prion' in FASTA format: 193 | 194 | >PRIO_HUMAN MAJOR PRION PROTEIN PRECURSOR 195 | MANLGCWMLVLFVATWSDLGLCKKRPKPGGWNTGGSRYPGQGSPGGNRYPPQGGGGWGQP 196 | HGGGWGQPHGGGWGQPHGGGWGQPHGGGWGQGGGTHSQWNKPSKPKTNMKHMAGAAAAGA 197 | VVGGLGGYMLGSAMSRPIIHFGSDYEDRYYRENMHRYPNQVYYRPMDEYSNQNNFVHDCV 198 | NITIKQHTVTTTTKGENFTETDVKMMERVVEQMCITQYERESQAYYQRGSSMVLFSSPPV 199 | ILLISFLIFLIVG 200 | 201 | The command line: 202 | 203 | seg prion 204 | 205 | gives the standard output below 206 | 207 | 208 | >PRIO_HUMAN MAJOR PRION PROTEIN PRECURSOR 209 | 210 | 1-49 MANLGCWMLVLFVATWSDLGLCKKRPKPGG 211 | WNTGGSRYPGQGSPGGNRY 212 | ppqggggwgqphgggwgqphgggwgqphgg 50-94 213 | gwgqphgggwgqggg 214 | 95-112 THSQWNKPSKPKTNMKHM 215 | agaaaagavvgglggymlgsams 113-135 216 | 136-187 RPIIHFGSDYEDRYYRENMHRYPNQVYYRP 217 | MDEYSNQNNFVHDCVNITIKQH 218 | tvttttkgenftet 188-201 219 | 202-236 DVKMMERVVEQMCITQYERESQAYYQRGSS 220 | MVLFS 221 | sppvillisflifliv 237-252 222 | 253-253 G 223 | 224 | The low-complexity sequences are on the left (lower case) and 225 | high-complexity sequences are on the right (upper case). All 226 | sequence segments read from left to right and their order in the 227 | sequence is from top to bottom, as shown by the central column of 228 | residue numbers. 229 | 230 | The command line: 231 | 232 | seg prion -x 233 | 234 | gives the following FASTA-formatted file:- 235 | 236 | >PRIO_HUMAN MAJOR PRION PROTEIN PRECURSOR 237 | MANLGCWMLVLFVATWSDLGLCKKRPKPGGWNTGGSRYPGQGSPGGNRYxxxxxxxxxxx 238 | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxTHSQWNKPSKPKTNMKHMxxxxxxxx 239 | xxxxxxxxxxxxxxxRPIIHFGSDYEDRYYRENMHRYPNQVYYRPMDEYSNQNNFVHDCV 240 | NITIKQHxxxxxxxxxxxxxxDVKMMERVVEQMCITQYERESQAYYQRGSSMVLFSxxxx 241 | xxxxxxxxxxxxG 242 | 243 | 244 | 245 | SEE ALSO 246 | -------- 247 | 248 | segn, blast, saps, xnu 249 | 250 | 251 | AUTHORS 252 | ------- 253 | 254 | John Wootton: wootton@ncbi.nlm.nih.gov 255 | Scott Federhen: federhen@ncbi.nlm.nih.gov 256 | 257 | National Center for Biotechnology Information 258 | Building 38A, Room 8N805 259 | National Library of Medicine 260 | National Institutes of Health 261 | Bethesda, Maryland, MD 20894 262 | U.S.A. 263 | 264 | 265 | PRIMARY REFERENCE 266 | ----------------- 267 | 268 | Wootton, J.C., Federhen, S. (1993) Statistics of local complexity 269 | in amino acid sequences and sequence databases. Computers & 270 | Chemistry 17: 149-163. 271 | 272 | 273 | OTHER REFERENCES 274 | ---------------- 275 | 276 | Wootton, J.C. (1994) Non-globular domains in protein sequences: 277 | automated segmentation using complexity measures. Computers & 278 | Chemistry 18: (in press). 279 | 280 | Altschul, S.F., Boguski, M., Gish, W., Wootton, J.C. (1994) Issues 281 | in searching molecular sequence databases. Nature Genetics 6: 282 | 119-129. 283 | 284 | Wootton, J.C. (1994) Simple sequences of protein and DNA. In: 285 | Nucleic Acid and Protein Sequence Analysis: A Practical Approach. 286 | (Second Edition, Chapter 8, Bishop, M.J. and Rawlings, C.R. Eds. 287 | IRL Press, Oxford) (In press). 288 | 289 | 290 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/SEG/src.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/SEG/src.zip -------------------------------------------------------------------------------- /src/mobidb_lite/bin/TISEAN/COPYING: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /src/mobidb_lite/bin/TISEAN/TISEAN_2.1.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/TISEAN/TISEAN_2.1.tar.gz -------------------------------------------------------------------------------- /src/mobidb_lite/bin/TISEAN/sav_gol: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/bin/TISEAN/sav_gol -------------------------------------------------------------------------------- /src/mobidb_lite/consensus.py: -------------------------------------------------------------------------------- 1 | import math 2 | import os 3 | import re 4 | import sys 5 | from tempfile import mkstemp 6 | from typing import Union 7 | from concurrent.futures import ThreadPoolExecutor, as_completed 8 | from functools import reduce 9 | 10 | from mobidb_lite import disembl, espritz, globplot, iupred, seg, anchor 11 | 12 | try: 13 | from mobidb_lite import nu_svr 14 | except ImportError: 15 | pass 16 | 17 | 18 | _THRESHOLDS = { 19 | "mobidblite": 0.625, 20 | "disembl-hl": 0.086, 21 | "disembl-rem465": 0.5, 22 | "espritz-d": 0.5072, 23 | "espritz-n": 0.3089, 24 | "espritz-x": 0.1434, 25 | "globplot": 0, 26 | "iupred-l": 0.5, 27 | "iupred-s": 0.5, 28 | "seg": 0.5, 29 | "anchor": 0.5 30 | } 31 | 32 | _MOBIDB_NAMES = { 33 | "majority": "prediction-disorder-th_50", 34 | "mobidblite": "prediction-disorder-mobidb_lite", 35 | "disembl-hl": "prediction-disorder-disHL", 36 | "disembl-rem465": "prediction-disorder-dis465", 37 | "espritz-d": "prediction-disorder-espD", 38 | "espritz-n": "prediction-disorder-espN", 39 | "espritz-x": "prediction-disorder-espX", 40 | "globplot": "prediction-disorder-glo", 41 | "iupred-l": "prediction-disorder-iupl", 42 | "iupred-s": "prediction-disorder-iups", 43 | "seg": "prediction-low_complexity-seg", 44 | "anchor": "prediction-lip-anchor", 45 | "Polyampholyte": "prediction-polyampholyte-mobidb_lite_sub", 46 | "Positive Polyelectrolyte": "prediction-positive_polyelectrolyte-mobidb_lite_sub", 47 | "Negative Polyelectrolyte": "prediction-negative_polyelectrolyte-mobidb_lite_sub", 48 | "Cysteine-rich": "prediction-cysteine_rich-mobidb_lite_sub", 49 | "Proline-rich": "prediction-proline_rich-mobidb_lite_sub", 50 | "Glycine-rich": "prediction-glycine_rich-mobidb_lite_sub", 51 | "Low complexity": "prediction-low_complexity-mobidb_lite_sub", 52 | "Polar": "prediction-polar-mobidb_lite_sub", 53 | "Extended": "prediction-extended-mobidb_lite_sub", 54 | "Compact": "prediction-compact-mobidb_lite_sub" 55 | } 56 | 57 | _CONSENSUS = frozenset( 58 | ["disembl-hl", "disembl-rem465", "espritz-d", "espritz-n", "espritz-x", "globplot", "iupred-l", "iupred-s"]) 59 | 60 | _POSITIVE_FLAG = "1" 61 | _NEGATIVE_FLAG = "0" 62 | _POSITIVE_RESIDUES = {"H", "R", "K"} 63 | _NEGATIVE_RESIDUES = {"D", "E"} 64 | _FEATURES = [ 65 | "Polyampholyte", 66 | "Positive Polyelectrolyte", 67 | "Negative Polyelectrolyte", 68 | "Cysteine-rich", 69 | "Proline-rich", 70 | "Glycine-rich", 71 | "Low complexity", 72 | "Polar", 73 | "Extended", 74 | "Compact" 75 | ] 76 | 77 | _VALID_AA = re.compile('[^RDNEKHQSCGTAMYVWLIPF]') 78 | 79 | def parse_fasta(file): 80 | seq_id = sequence = "" 81 | 82 | with file as fh: 83 | for line in map(str.rstrip, fh): 84 | if line[0] == ">": 85 | if seq_id and sequence: 86 | yield seq_id, sequence.upper() 87 | seq_id = line[1:].split()[0] 88 | sequence = "" 89 | elif line: 90 | sequence += line 91 | 92 | if seq_id and sequence: 93 | yield seq_id, sequence.upper() 94 | 95 | 96 | def run_predictors(sequence: str, bindir: str, **kwargs) -> dict: 97 | tempdir = kwargs.get("tempdir") 98 | run_extra = kwargs.get("extra", False) 99 | round_score = kwargs.get("round", False) 100 | 101 | fd, disbin = mkstemp(dir=tempdir) 102 | with open(fd, "wt") as fh: 103 | fh.write(f"1\n{len(sequence)}\n{sequence}") 104 | 105 | hot_loop, remark_465 = disembl.run(os.path.join(bindir, "DisEMBL"), 106 | os.path.join(bindir, "TISEAN"), 107 | sequence) 108 | 109 | results = {"disembl-hl": hot_loop, "disembl-rem465": remark_465, 110 | "espritz-d": espritz.run_espritz_d(os.path.join(bindir, "ESpritz"), disbin), 111 | "espritz-n": espritz.run_espritz_n(os.path.join(bindir, "ESpritz"), disbin), 112 | "espritz-x": espritz.run_espritz_x(os.path.join(bindir, "ESpritz"), disbin), 113 | "globplot": globplot.run(os.path.join(bindir, "TISEAN"), sequence), 114 | "iupred-l": iupred.run_long(os.path.join(bindir, "IUPred"), sequence), 115 | "iupred-s": iupred.run_short(os.path.join(bindir, "IUPred"), sequence)} 116 | # "fess": fess.run_fess(os.path.join(bindir, "FeSS"), disbin)} 117 | 118 | os.unlink(disbin) 119 | 120 | 121 | fd, fasta = mkstemp(dir=tempdir) 122 | with open(fd, "wt") as fh: 123 | fh.write(f">1\n{sequence}\n") 124 | 125 | results["seg"] = seg.run(os.path.join(bindir, "SEG"), fasta) 126 | 127 | if run_extra: 128 | results['anchor'] = anchor.run_anchor(os.path.join(bindir, "ANCHOR"), fasta) 129 | 130 | os.unlink(fasta) 131 | 132 | states = {} 133 | for k in results: 134 | states[k] = [(round(s, 3) if round_score else s) >= _THRESHOLDS[k] for s in results[k]] 135 | 136 | return results, states 137 | 138 | 139 | def run(file: str, bindir: str, datadir: str, threads: int, **kwargs): 140 | 141 | # Import model nu data fro compact/extended calculation 142 | if kwargs.get("extra", False): 143 | model_nu = nu_svr.load(os.path.join(datadir, "svr_model_nu_2.joblib")) 144 | kwargs["model_nu"] = model_nu 145 | 146 | if threads > 1: 147 | with ThreadPoolExecutor(max_workers=threads) as executor: 148 | fs = {} 149 | for seq_id, sequence in parse_fasta(file): 150 | f = executor.submit(predict, seq_id, sequence, bindir, **kwargs) 151 | fs[f] = seq_id 152 | 153 | if len(fs) == 1000: 154 | for f in as_completed(fs): 155 | seq_id = fs[f] 156 | regions, scores = f.result() 157 | yield seq_id, regions, scores 158 | 159 | fs.clear() 160 | 161 | for f in as_completed(fs): 162 | seq_id = fs[f] 163 | regions, scores = f.result() 164 | yield seq_id, regions, scores 165 | else: 166 | for seq_id, sequence in parse_fasta(file): 167 | regions, scores = predict(seq_id, sequence, bindir, **kwargs) 168 | yield seq_id, regions, scores 169 | 170 | 171 | def predict(sequence_id: str, sequence: str, bindir: str, **kwargs): 172 | force_consensus = kwargs.get("force", False) 173 | round_score = kwargs.get("round", False) 174 | run_extra = kwargs.get("run_extra", False) 175 | tempdir = kwargs.get("tempdir") 176 | threshold = kwargs.get("threshold", _THRESHOLDS["mobidblite"]) 177 | model_nu = kwargs.get("model_nu", None) 178 | 179 | seq_length = len(sequence) 180 | scores, scores_states = run_predictors(sequence, bindir, extra=run_extra, tempdir=tempdir) 181 | 182 | # Sub-regions 183 | if len(sequence) == len(scores_states["seg"]): 184 | features = get_region_features(sequence, scores_states["seg"]) 185 | else: 186 | features = None 187 | 188 | # Predictors agreement (subset of methods) 189 | agreement = [0] * seq_length 190 | num_indicators = 0 191 | 192 | for pred_name, pred_state in scores_states.items(): 193 | if pred_name in _CONSENSUS: 194 | if pred_state is None or len(pred_state) != seq_length: 195 | sys.stderr.write(f"{sequence_id}: {pred_name} excluded\n") 196 | continue 197 | 198 | num_indicators += 1 199 | for i, state in enumerate(pred_state): 200 | agreement[i] += state 201 | 202 | if num_indicators == 0: 203 | # All methods crashed 204 | sys.stderr.write(f"{sequence_id}: all methods failed\n") 205 | return None, None 206 | elif num_indicators < len(_CONSENSUS) and not force_consensus: 207 | # Some methods crashed and --force is not selected 208 | sys.stderr.write(f"{sequence_id}: some methods failed and --force parameters has not been selected\n") 209 | return None, None 210 | 211 | # Consensus states 212 | states = "" 213 | states_majority = "" 214 | scores["mobidblite"] = [] 215 | for s in agreement: 216 | if round_score: 217 | score = round(s / num_indicators, 3) 218 | else: 219 | score = s / num_indicators 220 | scores["mobidblite"].append(score) 221 | 222 | if score >= threshold: 223 | states += _POSITIVE_FLAG 224 | else: 225 | states += _NEGATIVE_FLAG 226 | 227 | if score >= 0.5: 228 | states_majority += _POSITIVE_FLAG 229 | else: 230 | states_majority += _NEGATIVE_FLAG 231 | 232 | 233 | # Majority consensus 234 | scores_states["majority"] = states_majority 235 | 236 | # Post-processing 237 | states = dilate(states, max_length=3) 238 | states = erode(states, max_length=3) 239 | states = merge_long_disordered_regions(states) 240 | 241 | # Convert states lists into strings 242 | scores_states = {pred_name: "".join([str(int(s)) for s in state]) for pred_name, state in scores_states.items()} 243 | scores_states["mobidblite"] = states 244 | 245 | results = {} 246 | 247 | for pred_name, pred_state in scores_states.items(): 248 | if pred_state is None or len(pred_state) != seq_length: 249 | sys.stderr.write(f"{sequence_id}: {pred_name} excluded\n") 250 | continue 251 | 252 | # Regions 253 | if pred_name == "mobidblite": 254 | regions = get_regions(pred_state, min_length=20) 255 | else: 256 | regions = get_regions(pred_state, min_length=1) 257 | 258 | for start, end, _ in sorted(regions): 259 | results.setdefault(pred_name, []).append((start + 1, end + 1)) 260 | 261 | # Sub-regions 262 | if features and pred_name == "mobidblite": 263 | region = features[start:end + 1] 264 | 265 | for i, j, state in get_regions(region, min_length=10): 266 | # state = _FEATURES[int(x)-1] 267 | results.setdefault(state, []).append((start + 1 + i, start + 1 + j)) 268 | 269 | # Nu parameters 270 | seq = _VALID_AA.sub('', sequence[start:end + 1]) # Remove non-standard AA from the sequence fragment 271 | if pred_name == "mobidblite" and model_nu and 30 <= len(seq) <= 1500: 272 | 273 | # calculate sequence features 274 | scd, shd, kappa, fcr, mean_lambda = nu_svr.calc_seq_properties(seq) 275 | # calculate scaling exponent 276 | nu = model_nu.predict([[scd, shd, kappa, fcr, mean_lambda]])[0] 277 | 278 | # "compact" se nu <= 0.475 e "extended" se nu > 0.55 279 | if nu <= 0.475: 280 | results.setdefault("Compact", []).append((start + 1, end + 1, nu)) 281 | elif nu > 0.55: 282 | results.setdefault("Extended", []).append((start + 1, end + 1, nu)) 283 | 284 | return results, scores 285 | 286 | 287 | def dilate(states: str, max_length: int) -> str: 288 | states = "{0}{1}{0}".format(_POSITIVE_FLAG * max_length, states) 289 | 290 | for level in range(1, max_length + 1): 291 | old = "{0}{1}{0}".format(_POSITIVE_FLAG * level, 292 | _NEGATIVE_FLAG * level) 293 | new = "{0}{0}{0}".format(_POSITIVE_FLAG * level) 294 | 295 | for _ in range(level + 1): 296 | states = states.replace(old, new) 297 | 298 | return states[max_length:-max_length] 299 | 300 | 301 | def erode(states: str, max_length: int) -> str: 302 | states = "{0}{1}{0}".format(_NEGATIVE_FLAG * max_length, states) 303 | 304 | for level in range(1, max_length + 1): 305 | old = "{0}{1}{0}".format(_NEGATIVE_FLAG * level, 306 | _POSITIVE_FLAG * level) 307 | new = "{0}{0}{0}".format(_NEGATIVE_FLAG * level) 308 | 309 | for _ in range(level + 1): 310 | states = states.replace(old, new) 311 | 312 | return states[max_length:-max_length] 313 | 314 | 315 | def merge_long_disordered_regions(states: str) -> str: 316 | while True: 317 | new_states = re.sub( 318 | pattern=r"{p}{{21,}}{n}{{1,10}}{p}{{21,}}".format( 319 | p=_POSITIVE_FLAG, 320 | n=_NEGATIVE_FLAG 321 | ), 322 | repl=_repl_struct_by_disord, 323 | string=states) 324 | 325 | if new_states == states: 326 | return new_states 327 | 328 | states = new_states 329 | 330 | 331 | def _repl_struct_by_disord(match: re.match): 332 | return _POSITIVE_FLAG * len(match.group(0)) 333 | 334 | 335 | def get_regions(states: Union[list, str], min_length: int) -> list: 336 | # Return 0-indexed regions 337 | regions = [] 338 | start = None 339 | current_flag = None 340 | for i, flag in enumerate(states): 341 | if flag != current_flag: 342 | if start is not None and current_flag != _NEGATIVE_FLAG: 343 | end = i - 1 344 | length = end - start + 1 345 | if length >= min_length: 346 | regions.append((start, end, current_flag)) 347 | 348 | start = i 349 | current_flag = flag 350 | 351 | if start is not None and current_flag != _NEGATIVE_FLAG: 352 | end = len(states) - 1 353 | length = end - start + 1 354 | if length >= min_length: 355 | regions.append((start, end, current_flag)) 356 | 357 | return regions 358 | 359 | 360 | def get_region_features(sequence: str, seg_states: list) -> list: 361 | 362 | all_features = {} 363 | for state in _FEATURES: 364 | all_features[state] = [_NEGATIVE_FLAG] * len(sequence) 365 | 366 | features = [_NEGATIVE_FLAG] * len(sequence) 367 | 368 | for i, seq in _bin(sequence, size=7): 369 | """ 370 | Classify sequence in one of the disorder states 371 | 372 | See Fig 7 from Das & Pappu (2013) 373 | https://www.pnas.org/doi/full/10.1073/pnas.1304749110 374 | """ 375 | f_plus = f_minus = 0 376 | 377 | for aa in seq: 378 | if aa in _POSITIVE_RESIDUES: 379 | f_plus += 1 380 | elif aa in _NEGATIVE_RESIDUES: 381 | f_minus += 1 382 | 383 | f_plus /= len(seq) 384 | f_minus /= len(seq) 385 | # fraction of charged residues 386 | fcr = f_plus + f_minus 387 | # net charge per residue 388 | ncpr = abs(f_plus - f_minus) 389 | 390 | state = None 391 | if fcr > 0.35: 392 | if ncpr <= 0.35: 393 | # Strong polyampholyte 394 | state = "Polyampholyte" 395 | elif f_plus > 0.35: 396 | # Strong positive polyelectrolyte 397 | state = "Positive Polyelectrolyte" 398 | elif f_minus > 0.35: 399 | # Strong negative polyelectrolyte 400 | state = "Negative Polyelectrolyte" 401 | else: 402 | raise ValueError(f"{seq}: {f_plus}, {f_minus}") 403 | else: 404 | # Weak polyampholyte/polyelectrolyte 405 | cysteine = proline = glycine = polar = 0 406 | for aa in seq: 407 | if aa == "C": 408 | cysteine += 1 409 | elif aa == "P": 410 | proline += 1 411 | elif aa == "G": 412 | glycine += 1 413 | elif aa in {"N", "Q", "S", "T"}: 414 | polar += 1 415 | 416 | if cysteine / len(seq) >= 0.32: 417 | state = "Cysteine-rich" 418 | elif proline / len(seq) >= 0.32: 419 | state = "Proline-rich" 420 | elif glycine / len(seq) >= 0.32: 421 | state = "Glycine-rich" 422 | elif seg_states[i]: 423 | state = "Low complexity" 424 | elif polar / len(seq) >= 0.32: 425 | state = "Polar" 426 | 427 | if state: 428 | all_features[state][i] = _POSITIVE_FLAG 429 | 430 | for state in reversed(_FEATURES): 431 | states = "".join(all_features[state]) 432 | states = dilate(states, max_length=5) 433 | states = erode(states, max_length=5) 434 | 435 | # index = str(_FEATURES.index(state) + 1) 436 | for i, flag in enumerate(states): 437 | if flag == _POSITIVE_FLAG: 438 | features[i] = state 439 | 440 | return features 441 | 442 | 443 | def _bin(sequence: str, size: int): 444 | """ 445 | Split sequence in overlapping bins of size `size` 446 | :param sequence: 447 | :param size: 448 | :return: 449 | """ 450 | n = math.floor((size - 1) / 2) 451 | seq_length = len(sequence) 452 | for i in range(seq_length): 453 | if i < n: 454 | yield i, sequence[n::-1] + sequence[1:n + 1] 455 | elif i + n < seq_length: 456 | yield i, sequence[i - n:i + n + 1] 457 | else: 458 | x = seq_length - (i + n) + 1 459 | yield i, sequence[i - n:] + sequence[::-1][1:x + 1] 460 | 461 | 462 | def is_enriched(sequence: str, residues: set, threshold: float = 0.32): 463 | return len([aa for aa in sequence if aa in residues]) / len(sequence) >= threshold 464 | 465 | 466 | def content_count(regions): 467 | return reduce(lambda x, t: x + (t[1] - t[0] + 1), regions, 0) 468 | -------------------------------------------------------------------------------- /src/mobidb_lite/data/svr_model_nu_2.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BioComputingUP/MobiDB-lite/ff5ff3891a2c638a403056bc6e5d9b6e588511a7/src/mobidb_lite/data/svr_model_nu_2.joblib -------------------------------------------------------------------------------- /src/mobidb_lite/disembl.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | from mobidb_lite.tisean import smooth 5 | 6 | 7 | """ 8 | Reference: http://dis.embl.de/html/paper.html 9 | """ 10 | 11 | ALPHABET = "ACDEFGHIKLMNPQRSTVWY" 12 | 13 | 14 | def run(disembl_bindir: str, tisean_bindir: str, sequence: str, ): 15 | # Conversion of network output to probability scores 16 | cmd = [os.path.join(disembl_bindir, "disembl")] 17 | proc = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=DEVNULL, encoding="utf-8") 18 | out, _ = proc.communicate(input=sequence + "\n") 19 | 20 | if proc.returncode != 0: 21 | return None, None 22 | 23 | hot_loop = [0] * len(sequence) 24 | remark_465 = [0] * len(sequence) 25 | results = list(out.splitlines()) 26 | 27 | j = 0 28 | for i, aa in enumerate(sequence): 29 | if aa in ALPHABET: 30 | values = results[j].split("\t") 31 | hot_loop[i] = float(values[1]) 32 | remark_465[i] = float(values[2]) 33 | j += 1 34 | 35 | return ( 36 | _smooth(tisean_bindir, hot_loop), 37 | _smooth(tisean_bindir, remark_465) 38 | ) 39 | 40 | 41 | def _smooth(bindir, scores): 42 | smoothed_scores = smooth(bindir, 0, 8, scores) 43 | if smoothed_scores: 44 | return [max(v, 0) for v in smoothed_scores] 45 | 46 | return None 47 | -------------------------------------------------------------------------------- /src/mobidb_lite/espritz.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def parse(output): 6 | c = {"D", "O"} 7 | scores = [] 8 | for line in output.splitlines(): 9 | if line and line[0] in c: 10 | _, score = line.split("\t") 11 | scores.append(float(score)) 12 | 13 | return scores 14 | 15 | 16 | def run_espritz_d(bindir: str, file: str): 17 | cmd = [ 18 | os.path.join(bindir, "disbinD"), 19 | os.path.join(bindir, "model_definition", "ensembleD"), 20 | file, 21 | "/dev/null" 22 | ] 23 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, cwd=bindir, encoding="utf-8") 24 | out, err = proc.communicate() 25 | return parse(out) if proc.returncode == 0 else None 26 | 27 | 28 | def run_espritz_n(bindir: str, file: str): 29 | cmd = [ 30 | os.path.join(bindir, "disbinN"), 31 | os.path.join(bindir, "model_definition", "ensembleN"), 32 | file, 33 | "/dev/null" 34 | ] 35 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, cwd=bindir, encoding="utf-8") 36 | out, err = proc.communicate() 37 | return parse(out) if proc.returncode == 0 else None 38 | 39 | 40 | def run_espritz_x(bindir: str, file: str): 41 | cmd = [ 42 | os.path.join(bindir, "disbinX"), 43 | os.path.join(bindir, "model_definition", "ensembleX"), 44 | file, 45 | "/dev/null" 46 | ] 47 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, cwd=bindir, encoding="utf-8") 48 | out, err = proc.communicate() 49 | return parse(out) if proc.returncode == 0 else None 50 | -------------------------------------------------------------------------------- /src/mobidb_lite/fess.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def parse(output): 6 | c = {"D", "O"} 7 | scores = [] 8 | for line in output.splitlines(): 9 | if line and line[0] in c: 10 | _, score = line.split("\t") 11 | scores.append(float(score)) 12 | 13 | return scores 14 | 15 | 16 | def run_fess(bindir: str, file: str): 17 | cmd = [ 18 | os.path.join(bindir, "fess"), 19 | os.path.join(bindir, "model_fastss"), 20 | file, 21 | "/dev/null" 22 | ] 23 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, cwd=bindir, encoding="utf-8") 24 | out, err = proc.communicate() 25 | print(cmd) 26 | print(out) 27 | print(err) 28 | return parse(out) if proc.returncode == 0 else None 29 | 30 | -------------------------------------------------------------------------------- /src/mobidb_lite/globplot.py: -------------------------------------------------------------------------------- 1 | from mobidb_lite.tisean import smooth 2 | 3 | RUSSEL_LINDING = { 4 | 'A': -0.261538461538462, 5 | 'C': -0.0151515151515152, 6 | 'D': 0.227629796839729, 7 | 'E': -0.204684629516228, 8 | 'F': -0.225572305974316, 9 | 'G': 0.433225711769886, 10 | 'H': -0.00121743364986608, 11 | 'I': -0.422234699606962, 12 | 'K': -0.100092289621613, 13 | 'L': -0.337933495925287, 14 | 'M': -0.225903614457831, 15 | 'N': 0.229885057471264, 16 | 'P': 0.552316012226663, 17 | 'Q': -0.187676577424997, 18 | 'R': -0.176592654077609, 19 | 'S': 0.142883029808825, 20 | 'T': 0.00887797506611258, 21 | 'V': -0.386174834235195, 22 | 'W': -0.243375458622095, 23 | 'Y': -0.20750516775322 24 | } 25 | 26 | 27 | def run(bindir: str, sequence: str, smooth_frame: int = 10): 28 | scores = [] 29 | s = 0 30 | for aa in sequence: 31 | s += RUSSEL_LINDING.get(aa, 0) 32 | scores.append(s) 33 | 34 | sg_scores = smooth(bindir, derivative=1, smooth_frame=smooth_frame, 35 | scores=scores) 36 | if sg_scores: 37 | head = scores[:smooth_frame] 38 | tail = scores[-smooth_frame:] 39 | _head = [] 40 | _tail = [] 41 | 42 | for i, (h, t) in enumerate(zip(head, tail)): 43 | try: 44 | x = (head[i + 1] - h) / 2 45 | except IndexError: 46 | x = (h - head[i - 1]) / 2 47 | finally: 48 | _head.append(x) 49 | 50 | try: 51 | x = (tail[i + 1] - t) / 2 52 | except IndexError: 53 | x = (t - tail[i - 1]) / 2 54 | finally: 55 | _tail.append(x) 56 | 57 | sg_scores[:smooth_frame] = _head 58 | sg_scores[-smooth_frame:] = _tail 59 | 60 | return sg_scores 61 | -------------------------------------------------------------------------------- /src/mobidb_lite/iupred.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def _run(bindir: str, sequence: str, mode: str): 6 | cmd = [ 7 | os.path.join(bindir, "iupred_string"), 8 | sequence, 9 | mode 10 | ] 11 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, env={"IUPred_PATH": bindir}, 12 | encoding="utf-8") 13 | out, err = proc.communicate() 14 | 15 | if proc.returncode == 0: 16 | scores = [] 17 | for line in out.splitlines(): 18 | if line and line[0] != "#": 19 | pos, aa, score = line.split() 20 | scores.append(float(score)) 21 | 22 | return scores 23 | 24 | return None 25 | 26 | 27 | def run_long(bindir: str, sequence: str): 28 | return _run(bindir, sequence, "long") 29 | 30 | 31 | def run_short(bindir: str, sequence: str): 32 | return _run(bindir, sequence, "short") 33 | -------------------------------------------------------------------------------- /src/mobidb_lite/nu_svr.py: -------------------------------------------------------------------------------- 1 | # Giulio Tesei, Sören von Bülow, Kresten Lindorff-Larsen 2024 2 | 3 | from joblib import dump, load 4 | import numpy as np 5 | import pandas as pd 6 | import random 7 | import numba as nb 8 | 9 | 10 | @nb.jit(nopython=True) 11 | def calc_fcr_and_ncpr(qs): 12 | fcr = np.mean(np.abs(qs)) 13 | ncpr = np.mean(qs) 14 | return fcr, ncpr 15 | 16 | 17 | @nb.jit(nopython=True) 18 | def calc_sigma(qs): 19 | fcr, ncpr = calc_fcr_and_ncpr(qs) 20 | if fcr == 0: 21 | return 0. 22 | else: 23 | return ncpr ** 2 / fcr 24 | 25 | 26 | @nb.jit(nopython=True) 27 | def calc_delta_form(qs, window=5): 28 | sig_m = calc_sigma(qs) 29 | 30 | nw = len(qs) - window + 1 31 | sigs = np.zeros((nw)) 32 | 33 | for idx in range(0, nw): 34 | q_window = qs[idx:idx + window] 35 | sigs[idx] = calc_sigma(q_window) 36 | delta = np.sum((sigs - sig_m) ** 2) / nw 37 | return delta 38 | 39 | 40 | @nb.jit(nopython=True) 41 | def calc_delta(qs): 42 | d5 = calc_delta_form(qs, window=5) 43 | d6 = calc_delta_form(qs, window=6) 44 | return (d5 + d6) / 2. 45 | 46 | 47 | @nb.jit(nopython=True) 48 | def get_qs_fast(seq): 49 | qs = np.zeros(len(seq)) 50 | 51 | # loop through sequence 52 | for idx in range(len(seq)): 53 | if seq[idx] in ['R', 'K']: 54 | qs[idx] = 1. 55 | elif seq[idx] in ['E', 'D']: 56 | qs[idx] = -1. 57 | else: 58 | qs[idx] = 0. 59 | return qs 60 | 61 | 62 | @nb.jit(nopython=True) 63 | def check_dmax(seq, dmax, seqmax): 64 | qs = get_qs_fast(seq) 65 | d = calc_delta(qs) 66 | if d > dmax: 67 | return seq, d 68 | else: 69 | return seqmax, dmax 70 | 71 | 72 | @nb.jit(nopython=True) 73 | def calc_case0(seqpos, seqneg, seqneu): 74 | seqmax = '' 75 | dmax = 0. 76 | N = len(seqpos) + len(seqneg) + len(seqneu) 77 | if len(seqpos) == 0: 78 | seqcharge = seqneg[:] 79 | elif len(seqneg) == 0: 80 | seqcharge = seqpos[:] 81 | if len(seqneu) > len(seqcharge): 82 | for pos in range(0, N - len(seqcharge) + 1): 83 | seqout = '' 84 | seqout += seqneu[:pos] 85 | seqout += seqcharge 86 | seqout += seqneu[pos:] 87 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 88 | else: 89 | for pos in range(0, N - len(seqneu) + 1): 90 | seqout = '' 91 | seqout += seqcharge[:pos] 92 | seqout += seqneu 93 | seqout += seqcharge[pos:] 94 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 95 | return seqmax 96 | 97 | 98 | @nb.jit(nopython=True) 99 | def calc_case1(seqpos, seqneg, seqneu): 100 | seqmax = '' 101 | dmax = 0. 102 | N = len(seqpos) + len(seqneg) + len(seqneu) 103 | if len(seqpos) > len(seqneg): 104 | for pos in range(0, N - len(seqneg) + 1): 105 | seqout = '' 106 | seqout += seqpos[:pos] 107 | seqout += seqneg 108 | seqout += seqpos[pos:] 109 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 110 | else: 111 | for neg in range(0, N - len(seqpos) + 1): 112 | seqout = '' 113 | seqout += seqneg[:neg] 114 | seqout += seqpos 115 | seqout += seqneg[neg:] 116 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 117 | return seqmax 118 | 119 | 120 | @nb.jit(nopython=True) 121 | def calc_case2(seqpos, seqneg, seqneu): 122 | seqmax = '' 123 | dmax = 0. 124 | for startNeuts in range(0, 7): 125 | for endNeuts in range(0, 7): 126 | startBlock = seqneu[:startNeuts] 127 | endBlock = seqneu[startNeuts:startNeuts + endNeuts] 128 | midBlock = seqneu[startNeuts + endNeuts:] 129 | 130 | seqout = '' 131 | seqout += startBlock 132 | seqout += seqpos 133 | seqout += midBlock 134 | seqout += seqneg 135 | seqout += endBlock 136 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 137 | return seqmax 138 | 139 | 140 | @nb.jit(nopython=True) 141 | def calc_case3(seqpos, seqneg, seqneu): 142 | seqmax = '' 143 | dmax = 0. 144 | for midNeuts in range(0, len(seqneu) + 1): 145 | midBlock = seqneu[:midNeuts] 146 | for startNeuts in range(0, len(seqneu) - midNeuts + 1): 147 | startBlock = seqneu[midNeuts:midNeuts + startNeuts] 148 | seqout = '' 149 | seqout += startBlock 150 | seqout += seqpos 151 | seqout += midBlock 152 | seqout += seqneg 153 | seqout += seqneu[midNeuts + startNeuts:] 154 | seqmax, dmax = check_dmax(seqout, dmax, seqmax) 155 | return seqmax 156 | 157 | 158 | def shuffle_str(seq): 159 | l = list(seq) 160 | random.shuffle(l) 161 | seq_shuffled = "".join(l) 162 | return (seq_shuffled) 163 | 164 | 165 | def split_seq(seq): 166 | """ split sequence in positive, negative, neutral residues """ 167 | seqpos = [] 168 | seqneg = [] 169 | seqneu = [] 170 | for s in seq: 171 | if s in ['K', 'R']: 172 | seqpos.append(s) 173 | elif s in ['D', 'E']: 174 | seqneg.append(s) 175 | else: 176 | seqneu.append(s) 177 | seqpos = shuffle_str(seqpos) 178 | seqneg = shuffle_str(seqneg) 179 | seqneu = shuffle_str(seqneu) 180 | return seqpos, seqneg, seqneu 181 | 182 | 183 | def construct_deltamax(seq): 184 | seqpos, seqneg, seqneu = split_seq(seq) 185 | 186 | if (len(seqpos) == 0) or (len(seqneg) == 0): 187 | seqmax = calc_case0(seqpos, seqneg, seqneu) 188 | elif len(seqneu) == 0: 189 | seqmax = calc_case1(seqpos, seqneg, seqneu) 190 | elif len(seqneu) >= 18: 191 | seqmax = calc_case2(seqpos, seqneg, seqneu) 192 | else: 193 | seqmax = calc_case3(seqpos, seqneg, seqneu) 194 | return seqmax 195 | 196 | 197 | def calc_fcr_and_kappa(seq): 198 | """ kappa parameter, Das & Pappu, PNAS 2013 199 | Here set to 0, instead of -1 as in localCIDER, for sequences 200 | with no charges or only pos/neg residues """ 201 | qs = get_qs_fast(seq) 202 | if np.all(qs == 0): 203 | return 0, 0 204 | else: 205 | fcr, _ = calc_fcr_and_ncpr(qs) 206 | seqpos, seqneg, seqneu = split_seq(seq) 207 | if (len(seqneu) == 0): 208 | if (len(seqneg) == 0) or (len(seqpos) == 0): 209 | return fcr, 0 210 | 211 | delta = calc_delta(qs) 212 | 213 | seq_max = construct_deltamax(seq) 214 | qs_max = get_qs_fast(seq_max) 215 | delta_max = calc_delta(qs_max) 216 | 217 | kappa = delta / delta_max 218 | return fcr, kappa 219 | 220 | 221 | @nb.jit(nopython=True) 222 | def calc_SCD(seq): 223 | """ Sequence charge decoration, eq. 14 in Sawle & Ghosh, JCP 2015 """ 224 | qs = get_qs_fast(seq) 225 | N = len(seq) 226 | scd = 0. 227 | for idx in range(1, N): 228 | for jdx in range(0, idx): 229 | s = qs[idx] * qs[jdx] * (idx - jdx) ** 0.5 230 | scd = scd + s 231 | scd = scd / N 232 | return scd 233 | 234 | 235 | @nb.jit(nopython=True) 236 | def calc_SHD(seq, lambda_map, beta=-1.): 237 | """ Sequence hydropathy decoration, eq. 4 in Zheng et al., JPC Letters 2020""" 238 | N = len(seq) 239 | shd = 0. 240 | for idx in range(0, N - 1): 241 | for jdx in range(idx + 1, N): 242 | s = lambda_map[idx, jdx] * (jdx - idx) ** beta 243 | shd = shd + s 244 | shd = shd / N 245 | return shd 246 | 247 | 248 | def calc_seq_properties(seq): 249 | lambdas = {"R": 0.7307624768, "D": 0.0416040481, "N": 0.425585901, "E": 0.0006935461, 250 | "K": 0.1790211739, "H": 0.4663667291, "Q": 0.3934318551, "S": 0.4625416812, 251 | "C": 0.5615435099, "G": 0.7058843734, "T": 0.3713162976, "A": 0.2743297969, 252 | "M": 0.5308481134, "Y": 0.9774611449, "V": 0.2083769608, "W": 0.989376474, 253 | "L": 0.6440005008, "I": 0.5423623611, "P": 0.3593126576, "F": 0.8672358982} 254 | lambdas = np.asarray(list(map(lambda x: lambdas[x], seq))) 255 | lambda_map = np.add.outer(lambdas, lambdas) 256 | scd = calc_SCD(seq) # Slow 257 | shd = calc_SHD(seq, lambda_map) 258 | fcr, kappa = calc_fcr_and_kappa(seq) 259 | mean_lambda = np.mean(lambdas) 260 | return scd, shd, kappa, fcr, mean_lambda 261 | 262 | 263 | if __name__ == "__main__": 264 | 265 | # load scikit-learn SVR model 266 | # model_nu = load('data/svr_model_nu.joblib') 267 | # dump(model_nu, open('data/svr_model_nu_2.joblib', 'wb')) 268 | 269 | model_nu = load('data/svr_model_nu_2.joblib') 270 | 271 | # sequence to analyse 272 | seq = "ISSENSNPEQDLKLTSEEESQRLKVSENSQPEKMSQEPEINKDCDREVEEEIKKHGSNPVGLPENLTNGASAGNGDDGLIPQRKSRKPENQQFPDTENEEYHSDEQNDTQKQLSEEQNTGISQDEILTNKQ" 273 | 274 | if len(seq) < 30 or len(seq) > 1500: 275 | print('Sequence length outside the range of the training set') 276 | 277 | else: 278 | # calculate sequence features 279 | scd, shd, kappa, fcr, mean_lambda = calc_seq_properties(seq) 280 | 281 | # calculate scaling exponent 282 | nu_svr = model_nu.predict([[scd, shd, kappa, fcr, mean_lambda]])[0] 283 | 284 | # print results 285 | results = pd.Series(data=[scd, shd, kappa, fcr, mean_lambda, nu_svr], 286 | index=['scd', 'shd', 'kappa', 'fcr', 'mean_lambda', 'nu_svr']) 287 | 288 | print(results) 289 | -------------------------------------------------------------------------------- /src/mobidb_lite/seg.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def run(bindir: str, file: str): 6 | cmd = [ 7 | os.path.join(bindir, "seg"), 8 | file, 9 | "-x" 10 | ] 11 | proc = Popen(cmd, stdout=PIPE, stderr=DEVNULL, encoding="utf-8") 12 | out, err = proc.communicate() 13 | 14 | if proc.returncode == 0: 15 | scores = [] 16 | for line in out.splitlines(): 17 | if line and line[0] != ">": 18 | for aa in line: 19 | scores.append(1 if aa == "x" else 0) 20 | 21 | return scores 22 | 23 | return None 24 | -------------------------------------------------------------------------------- /src/mobidb_lite/tisean.py: -------------------------------------------------------------------------------- 1 | import os 2 | from subprocess import DEVNULL, PIPE, Popen 3 | 4 | 5 | def smooth(bindir: str, derivative: int, smooth_frame: float, scores: list): 6 | # Perform a Savitzky-Golay filter 7 | 8 | if len(scores) < 2 * smooth_frame: 9 | smooth_frame = len(scores) // 2 10 | elif smooth_frame == 0: 11 | smooth_frame = 1 12 | 13 | cmd = [ 14 | os.path.join(bindir, "sav_gol"), 15 | "-V", "0", 16 | "-D", str(derivative), 17 | "-n", "{0},{0}".format(smooth_frame) 18 | ] 19 | proc = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=DEVNULL, encoding="utf-8") 20 | out, _ = proc.communicate(input="\n".join(map(str, scores)) + "\n") 21 | 22 | if proc.returncode == 0: 23 | return list(map(float, map(str.rstrip, out.splitlines()))) 24 | 25 | return None 26 | --------------------------------------------------------------------------------