├── LICENSE ├── README.md ├── human_5utrs ├── .ipynb_checkpoints │ ├── human_utr_modeling-checkpoint.ipynb │ └── snv_log_diff_with_phenotype_info-checkpoint.ipynb ├── data │ └── snv_phenotype_log_diff.csv ├── human_utr_modeling.ipynb └── snv_log_diff_with_phenotype_info.ipynb ├── modeling ├── .ipynb_checkpoints │ ├── training_MRL_CNN-checkpoint.ipynb │ └── training_poly_CNN-checkpoint.ipynb ├── Varying_length_25to100_modeling.ipynb ├── cds_modification_comparisons │ ├── .ipynb_checkpoints │ │ ├── compare_all_chemistries_avg_models-checkpoint.ipynb │ │ └── compare_cds-checkpoint.ipynb │ ├── compare_all_chemistries_avg_models.ipynb │ ├── compare_cds.ipynb │ └── saved_data │ │ ├── cds_c_model_performance.dict │ │ ├── cds_e_model_performance.dict │ │ ├── e1_model.h5 │ │ ├── e1_model_performance.dict │ │ ├── e2_model.h5 │ │ ├── e2_model_performance.dict │ │ ├── emp1_model.h5 │ │ ├── emp1_model_performance.dict │ │ ├── emp2_model.h5 │ │ ├── emp2_model_performance.dict │ │ ├── ep1_model.h5 │ │ ├── ep1_model_performance.dict │ │ ├── ep2_model.h5 │ │ ├── ep2_model_performance.dict │ │ └── model_performance.dict ├── saved_models │ ├── .ipynb_checkpoints │ │ └── README-checkpoint │ ├── README │ ├── Varying_length_25to100_model.hdf5 │ ├── evolution_model.hdf5 │ ├── main_MRL_model.hdf5 │ ├── polysome_profile_model.hdf5 │ ├── retrained_evolution_model.hdf5 │ └── retrained_main_MRL_model.hdf5 ├── training_MRL_CNN.ipynb └── training_poly_CNN.ipynb └── sequence_design ├── .ipynb_checkpoints ├── evolve_for_target_MRL-checkpoint.ipynb └── stepwise_evolve_utrs-checkpoint.ipynb ├── evolve_for_target_MRL.ipynb └── stepwise_evolve_utrs.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Human 5′ UTR design and variant effect prediction from a massively parallel translation assay 2 | 3 | Predicting the impact of cis-regulatory sequence on gene expression is a foundational challenge for biology. We combine polysome profiling of hundreds of thousands of randomized 5′ UTRs with deep learning to build a predictive model that relates human 5′ UTR sequence to translation. Together with a genetic algorithm, we use the model to engineer new 5′ UTRs that accurately target specified levels of ribosome loading, providing the ability to tune sequences for optimal protein expression. We show that the same approach can be extended to chemically modified RNA, an important feature for applications in mRNA therapeutics and synthetic biology. We test 35,000 truncated human 5′ UTRs and 3,577 naturally-occurring variants and show that the model accurately predicts ribosome loading of these sequences. Finally, we provide evidence of 47 SNVs associated with human diseases that cause a significant change in ribosome loading and thus a plausible molecular basis for disease. 4 | 5 | https://www.nature.com/articles/s41587-019-0164-5 6 | 7 |

8 | 5-UTR-modeling 9 |

10 | 11 | All data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002 12 | 13 | After cloning the repository, create a new directory named 'data' and place .CSVs from the GEO link above. 14 | -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/.ipynb_checkpoints/compare_cds-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 1 6 | } 7 | -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/cds_c_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'c2_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\xb5a\x87\x89\x1a)\xeb?' 20 | tRp6 21 | g3 22 | (g5 23 | S'a/d\\\xf8p\xe9?' 24 | tRp7 25 | g3 26 | (g5 27 | S'7\xe0\x1b\xa7\x01\x10\xea?' 28 | tRp8 29 | g3 30 | (g5 31 | S'\xba\x8e~Y\xfbY\xea?' 32 | tRp9 33 | tp10 34 | sS'c1_model' 35 | p11 36 | (g3 37 | (g5 38 | S'$)i \xcf\x06\xec?' 39 | tRp12 40 | g3 41 | (g5 42 | S'zq\xa8\xd1\xa0$\xea?' 43 | tRp13 44 | g3 45 | (g5 46 | S'\xf7\x8a2".\x8d\xea?' 47 | tRp14 48 | g3 49 | (g5 50 | S'-d\r|\xc0\xc4\xe9?' 51 | tRp15 52 | tp16 53 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/cds_e_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'e1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\x15\xc8\xb3y\x14\x03\xed?' 20 | tRp6 21 | g3 22 | (g5 23 | S'\xf3#\xbc\xf7\x1e\x99\xeb?' 24 | tRp7 25 | g3 26 | (g5 27 | S'\x93\xbfK|\xc88\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S'\x9d\xb4\xa0\x992\x99\xe7?' 32 | tRp9 33 | tp10 34 | sS'e2_model' 35 | p11 36 | (g3 37 | (g5 38 | S'\xb7\x06n:\x90#\xec?' 39 | tRp12 40 | g3 41 | (g5 42 | S'\x13v\xec\xad\x02W\xec?' 43 | tRp13 44 | g3 45 | (g5 46 | S'w\x9ej\xe91?\xe6?' 47 | tRp14 48 | g3 49 | (g5 50 | S'\x82\x05\xdeD\x8c\xbc\xe5?' 51 | tRp15 52 | tp16 53 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/e1_model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/cds_modification_comparisons/saved_data/e1_model.h5 -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/e1_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\x94\x83^2Ee\xe9?' 20 | tRp6 21 | g3 22 | (g5 23 | S'u4\x01\x8fW(\xe8?' 24 | tRp7 25 | g3 26 | (g5 27 | S'\xed\x15\xae\xea%9\xe9?' 28 | tRp8 29 | g3 30 | (g5 31 | S'\x9a\xff}\xc5\xaa\xf1\xe9?' 32 | tRp9 33 | g3 34 | (g5 35 | S'\xdbf>3\xde"\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S'r9[\x0b\xd5\x8c\xe8?' 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'\xe2\t\x97\xea\xcc\xeb\xed?' 47 | tRp14 48 | g3 49 | (g5 50 | S'\xf4+j\xdf\x97\x0c\xec?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\x1b\xba\xfe/\xeb\xd8\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S'\x86\xf1\x84\xa1,Z\xe8?' 59 | tRp17 60 | g3 61 | (g5 62 | S'\x86\x1f\x91@}b\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S'N\xb4\xca\xb7\xfb\xca\xe8?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'\x98\xc7\xbe\xd1I\xcb\xec?' 74 | tRp22 75 | g3 76 | (g5 77 | S'\xc3\xd0\x1a\xf4s\xa1\xec?' 78 | tRp23 79 | g3 80 | (g5 81 | S'\x811*\x95\xff\x8d\xe6?' 82 | tRp24 83 | g3 84 | (g5 85 | S'\xa3U"i>\xc9\xe8?' 86 | tRp25 87 | g3 88 | (g5 89 | S'\xcd\xcf\x05\x0eU\xd4\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'\xe9/,\x83\xa2\x0e\xe9?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'\xea\xc4\xfa7\xab1\xea?' 101 | tRp30 102 | g3 103 | (g5 104 | S'\x1e\x83\x90\xdb\xa5u\xe9?' 105 | tRp31 106 | g3 107 | (g5 108 | S'>\x1b\x15\xcb6,\xe9?' 109 | tRp32 110 | g3 111 | (g5 112 | S'\x05\x89"\x8eG5\xeb?' 113 | tRp33 114 | g3 115 | (g5 116 | S'v6/\xfeW\xfc\xe7?' 117 | tRp34 118 | g3 119 | (g5 120 | S'Q\x11\xf2\xc6J\xa8\xe9?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'\x8d\xdc\x91\xdc\xc3\x18\xeb?' 128 | tRp38 129 | g3 130 | (g5 131 | S'\xf8~%O\xa1q\xea?' 132 | tRp39 133 | g3 134 | (g5 135 | S'\xe38\xd2\x1e<\x13\xe8?' 136 | tRp40 137 | g3 138 | (g5 139 | S'Y"|\xdeh\xd2\xe9?' 140 | tRp41 141 | g3 142 | (g5 143 | S'6\xfb\x8fn`\xe7\xe6?' 144 | tRp42 145 | g3 146 | (g5 147 | S'W\xac\x94W\xf6z\xea?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S'\x13)(\x9d\x93;\xe9?' 155 | tRp46 156 | g3 157 | (g5 158 | S'\xc0\x8f\x0f\xbdf\x90\xe7?' 159 | tRp47 160 | g3 161 | (g5 162 | S'y\xd3+\xc8\xb1\xcb\xe8?' 163 | tRp48 164 | g3 165 | (g5 166 | S'\xd35\x83\xccJ\\\xe9?' 167 | tRp49 168 | g3 169 | (g5 170 | S'd!\x9a;\xfcS\xe8?' 171 | tRp50 172 | g3 173 | (g5 174 | S'c\xb3\x12\x9e\xc2\xf7\xe7?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/e2_model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/cds_modification_comparisons/saved_data/e2_model.h5 -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/e2_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'#\xb2.\x08s6\xe8?' 20 | tRp6 21 | g3 22 | (g5 23 | S'\xf3\x8eu\xae\xbd\xaa\xe6?' 24 | tRp7 25 | g3 26 | (g5 27 | S'\xcf\xa2o\x02G\xde\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S'\x94\\-\x9d\xc6\xd2\xe8?' 32 | tRp9 33 | g3 34 | (g5 35 | S'\x81\x02&\xfe\xacT\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S'\xbf\x1c\xdb<\xce\x8e\xe7?' 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'f\x97\xd3c\x1cz\xed?' 47 | tRp14 48 | g3 49 | (g5 50 | S'$x%Z\xb0\xa3\xeb?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xc37\xad\xa4\xaf\xb6\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S'\x00c\xa7_\xdb\xc8\xe7?' 59 | tRp17 60 | g3 61 | (g5 62 | S'{m\xf8\x01$U\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S'\xd9"$\x0b\xe4\x81\xe8?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'\x8dG68\xdeY\xec?' 74 | tRp22 75 | g3 76 | (g5 77 | S'\xb0\x9b*\xcc87\xec?' 78 | tRp23 79 | g3 80 | (g5 81 | S'\x11\x94wc\xc0I\xe6?' 82 | tRp24 83 | g3 84 | (g5 85 | S'\xdc\x0e\xf3\xd1k\xfc\xe7?' 86 | tRp25 87 | g3 88 | (g5 89 | S'\xe9\xb7\x12JO\xbc\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'A\xf52TH\xa6\xe8?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'E\x87\xe6kH\xd7\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'Z\xfb\xc0^%(\xe9?' 105 | tRp31 106 | g3 107 | (g5 108 | S'\xceDp\xe0\x8d\xbe\xe8?' 109 | tRp32 110 | g3 111 | (g5 112 | S'3\xf2\x12W\x99h\xea?' 113 | tRp33 114 | g3 115 | (g5 116 | S'2\xc4\xfe\x8d"A\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'\xc6>)\x89\xa3\x0f\xe9?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'^=\xfcT6V\xea?' 128 | tRp38 129 | g3 130 | (g5 131 | S'\x05\xb1}L\x90\xea\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S'\x96\x0en\xb7Y\x9f\xe7?' 136 | tRp40 137 | g3 138 | (g5 139 | S';c\x9a@\xa1\x0e\xe9?' 140 | tRp41 141 | g3 142 | (g5 143 | S'\x91\x12q#\xf3\xd1\xe6?' 144 | tRp42 145 | g3 146 | (g5 147 | S'qp)\xb8\x03\n\xea?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S'\x02\xfb"\x02\x85\xb5\xe8?' 155 | tRp46 156 | g3 157 | (g5 158 | S'C\xbc\x16\x1a\xcf(\xe7?' 159 | tRp47 160 | g3 161 | (g5 162 | S'\xe5\x8f\xf0\xc52\x80\xe8?' 163 | tRp48 164 | g3 165 | (g5 166 | S'>\xe43\x96\xbf\xb5\xe8?' 167 | tRp49 168 | g3 169 | (g5 170 | S'^~\xe4X1\x91\xe8?' 171 | tRp50 172 | g3 173 | (g5 174 | S'.\xc4a)D\x8d\xe7?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/emp1_model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/cds_modification_comparisons/saved_data/emp1_model.h5 -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/emp1_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\xc3\x85 ;\xd6\xbf\xe7?' 20 | tRp6 21 | g3 22 | (g5 23 | S'\xdak\xe0\x17\x8b\xec\xe5?' 24 | tRp7 25 | g3 26 | (g5 27 | S'J\xc2\xb7\xc2\xdfm\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S"f,'\x00\x87\xb9\xe7?" 32 | tRp9 33 | g3 34 | (g5 35 | S'\xd8?\x19\xa3\x0c_\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S"A'y\xac\x18\x9c\xe6?" 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'\x08\x04@\xc7\x04\xe1\xec?' 47 | tRp14 48 | g3 49 | (g5 50 | S'\xa5\xe9\x97\x89Yj\xea?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xb9b\x95\x89.\x08\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S'\xf3\x8f5\xa0\x04\xcb\xe5?' 59 | tRp17 60 | g3 61 | (g5 62 | S'H\xb5\xa4\\\x90\x11\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S'\xfb\xd3M\xd3K\xf7\xe6?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'\xf85L`\xef2\xec?' 74 | tRp22 75 | g3 76 | (g5 77 | S'\x11\xe6\xbb_\xe0\xa7\xeb?' 78 | tRp23 79 | g3 80 | (g5 81 | S'\xb3\xbd\x984\xda\xd0\xe5?' 82 | tRp24 83 | g3 84 | (g5 85 | S'\xc3\xaa\xcax\xd2f\xe6?' 86 | tRp25 87 | g3 88 | (g5 89 | S'0:\x18\xb6\xd1\xc2\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'\x1a\xc0\x14\n\xa9v\xe7?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'&^\\\x91[K\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'n\xe5\x7f\x93\x8f\x17\xe8?' 105 | tRp31 106 | g3 107 | (g5 108 | S'\xc8\xa2\xb3\x03B\xe0\xe8?' 109 | tRp32 110 | g3 111 | (g5 112 | S'\xab\x05\x0eFj_\xe9?' 113 | tRp33 114 | g3 115 | (g5 116 | S'\xc2Rw\xa9\xf4\xcd\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'\xeaO\x84\xac\xef\x05\xe8?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'\xa2#\xb6\xb3.Q\xea?' 128 | tRp38 129 | g3 130 | (g5 131 | S'\x1cL\xa2K\x15f\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S'E-\xcb\x8f\xce\xa3\xe7?' 136 | tRp40 137 | g3 138 | (g5 139 | S'\xfeO2\x1cG\xf6\xe7?' 140 | tRp41 141 | g3 142 | (g5 143 | S'\x98\x8e\xee\x91\xd8L\xe7?' 144 | tRp42 145 | g3 146 | (g5 147 | S'Q\xf5\xf5\xe6\xeb`\xe9?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S'D\x04q\xc7\xb8\x80\xe7?' 155 | tRp46 156 | g3 157 | (g5 158 | S'\xa1\x8fr\x1e\x93l\xe5?' 159 | tRp47 160 | g3 161 | (g5 162 | S'\xbe~x\xd4\xdd\xbe\xe7?' 163 | tRp48 164 | g3 165 | (g5 166 | S'!\x19\xd8$\xcf\x14\xe7?' 167 | tRp49 168 | g3 169 | (g5 170 | S'\x99\x17\xb9\xce$"\xe8?' 171 | tRp50 172 | g3 173 | (g5 174 | S'\x94<5T\xf2\xd9\xe5?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/emp2_model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/cds_modification_comparisons/saved_data/emp2_model.h5 -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/emp2_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\xb8\xa1P\xe2k\xa3\xe8?' 20 | tRp6 21 | g3 22 | (g5 23 | S'Y\xfa\x18\xce\x98\x17\xe7?' 24 | tRp7 25 | g3 26 | (g5 27 | S'lY\xe8\x82\xb7\xdf\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S'k`\x04\xb7\x1b\x9d\xe8?' 32 | tRp9 33 | g3 34 | (g5 35 | S'\xfd\x11J\x1b\x93\x90\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S';\x7fED\x9d\xca\xe7?' 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'\x8b\xd4\x06\\|\xef\xec?' 47 | tRp14 48 | g3 49 | (g5 50 | S'q\x80\xaa\xd2\xed\xe7\xea?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xcf\x95\xfcS\x9b;\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S'.u\x10\x17\xdc\x93\xe6?' 59 | tRp17 60 | g3 61 | (g5 62 | S'B\xe4\x1e\x04\xc0\n\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S'\xa2AiYI\xe1\xe7?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'\x01\x96\xbe\xfa\x1f\xf0\xeb?' 74 | tRp22 75 | g3 76 | (g5 77 | S'`nU;a\x90\xeb?' 78 | tRp23 79 | g3 80 | (g5 81 | S'9`\x1al!\xb6\xe5?' 82 | tRp24 83 | g3 84 | (g5 85 | S'p<\xaa\x80\x15\xbd\xe6?' 86 | tRp25 87 | g3 88 | (g5 89 | S'\x84\xe1\xf3K c\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'1tZ\x12\xf9\xf1\xe7?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'\xd7p\x93?yl\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'0\x95\xd0\xc5z\x87\xe8?' 105 | tRp31 106 | g3 107 | (g5 108 | S'\xdf\xb3\x0e\x01\xd9\xb6\xe8?' 109 | tRp32 110 | g3 111 | (g5 112 | S'\xf3p\xdf\xbf\x96\x9f\xe9?' 113 | tRp33 114 | g3 115 | (g5 116 | S'\xf6\xeb\xees\xcf\x80\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'\x80\xa1\x8b\xa4;\xb8\xe8?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'\xf0&\xbb5\xb5;\xea?' 128 | tRp38 129 | g3 130 | (g5 131 | S'\xd74#\xe2\xf2\x8f\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S'.E\xdefS\xc2\xe7?' 136 | tRp40 137 | g3 138 | (g5 139 | S'\xde\x81\x13b\xf0\x84\xe8?' 140 | tRp41 141 | g3 142 | (g5 143 | S'_\x1a\xb7\x0bG<\xe7?' 144 | tRp42 145 | g3 146 | (g5 147 | S'\xcf\x89\xf8\tG\xd4\xe9?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S'd0\xd1(\xda\x08\xe8?' 155 | tRp46 156 | g3 157 | (g5 158 | S'\xac\x9b\xe08\x1eI\xe6?' 159 | tRp47 160 | g3 161 | (g5 162 | S'3\xa4`\xc1\xdfE\xe8?' 163 | tRp48 164 | g3 165 | (g5 166 | S'E\xe8\x90t\x80\xe7\xe7?' 167 | tRp49 168 | g3 169 | (g5 170 | S'\x99\xb0#\x0b\xcc\x8b\xe8?' 171 | tRp50 172 | g3 173 | (g5 174 | S'VZ\xb0\xdc\xb1\x03\xe7?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/ep1_model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/cds_modification_comparisons/saved_data/ep1_model.h5 -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/ep1_model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'\xae\xb7{7d\x00\xe8?' 20 | tRp6 21 | g3 22 | (g5 23 | S'4\x85"c\xe18\xe6?' 24 | tRp7 25 | g3 26 | (g5 27 | S'd\xd9\xf3\xbf\xb9\xff\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S'e\xe9\x9dB\x9eg\xe8?' 32 | tRp9 33 | g3 34 | (g5 35 | S'\xd9\xbe\x10u\x95\xb2\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S'm\xd8\xd0\x16;\xff\xe6?' 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'\xce\x96\x90\xf2\xb2\xf4\xec?' 47 | tRp14 48 | g3 49 | (g5 50 | S';\x19\xd6\xc1\x1b{\xea?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xf9\xc0\\\xcf\xebV\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S"\x97y\x17\x83\x86'\xe6?" 59 | tRp17 60 | g3 61 | (g5 62 | S'\xcfa\xd0\xc9\xd0]\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S']@\x9f{\x82O\xe7?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'$v\x9f\x04c\xf7\xeb?' 74 | tRp22 75 | g3 76 | (g5 77 | S'\xa3pM)}\xa1\xeb?' 78 | tRp23 79 | g3 80 | (g5 81 | S'\x88NzR\xd5\xd5\xe5?' 82 | tRp24 83 | g3 84 | (g5 85 | S'<)\x8fq\xac|\xe6?' 86 | tRp25 87 | g3 88 | (g5 89 | S'(,\xa2\xbc\xd9\xa1\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'%,\x83\x15\xa3\x7f\xe7?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'7\xd7\xd0h\xb37\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'\xf5Z\xde\xa47\x1b\xe8?' 105 | tRp31 106 | g3 107 | (g5 108 | S'\xfd\x92\xcf\x060%\xe9?' 109 | tRp32 110 | g3 111 | (g5 112 | S'm\x95=\x90\xe6\x97\xe9?' 113 | tRp33 114 | g3 115 | (g5 116 | S'\xcc\x1a\xc3\x00?\xed\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'\xa1-\t\x8ei7\xe8?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'\x10\xc3\xd5\xf134\xea?' 128 | tRp38 129 | g3 130 | (g5 131 | S']WJ\\\x12L\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S'h\xc1\x18\xed?' 47 | tRp14 48 | g3 49 | (g5 50 | S'K\x90\x94.\x8e\x00\xeb?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xfcwN\x1c\xec"\xe7?' 55 | tRp16 56 | g3 57 | (g5 58 | S'\x85\x95\xf3\xc4\x13\xc8\xe7?' 59 | tRp17 60 | g3 61 | (g5 62 | S'\xc5!\xbe b\xed\xe6?' 63 | tRp18 64 | g3 65 | (g5 66 | S'\x0b-\xd1OEU\xe8?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'B=R\xa6;\x11\xec?' 74 | tRp22 75 | g3 76 | (g5 77 | S'\x91\x88\x1c\xb5\x80\xe2\xeb?' 78 | tRp23 79 | g3 80 | (g5 81 | S's,\xc6O\x19\x94\xe6?' 82 | tRp24 83 | g3 84 | (g5 85 | S'\x90\x924\x07\xd7\x0c\xe8?' 86 | tRp25 87 | g3 88 | (g5 89 | S'KK0\x8d\xe4>\xe6?' 90 | tRp26 91 | g3 92 | (g5 93 | S'\x0emy\xc4\xfe\x9c\xe8?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'\x1bj\xda+\xcb\xe9\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'\xec1U\x7f\xbb\x19\xe9?' 105 | tRp31 106 | g3 107 | (g5 108 | S'J\x93\x04\xbf*g\xe9?' 109 | tRp32 110 | g3 111 | (g5 112 | S'\x95>\xa1\x84\xc2\xa3\xea?' 113 | tRp33 114 | g3 115 | (g5 116 | S'\xc6rUj\xb3\xe9\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'%\x1a\x82\xbd4H\xe9?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'\xc6_\xc7\xdb\x08\xa4\xea?' 128 | tRp38 129 | g3 130 | (g5 131 | S'\xb3\xbd\xb8\x1c\x7f\xbc\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S'\x14\x8a\n\x1e\x92|\xe8?' 136 | tRp40 137 | g3 138 | (g5 139 | S'n\x98Y\xf5,\x81\xe9?' 140 | tRp41 141 | g3 142 | (g5 143 | S'\xcd\xf2%\xee\xde\xe8\xe7?' 144 | tRp42 145 | g3 146 | (g5 147 | S'q\x82Czp\x1d\xea?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S'\x9bM\x924W\x1a\xe9?' 155 | tRp46 156 | g3 157 | (g5 158 | S'\xb7\x9a \x1d!\x10\xe7?' 159 | tRp47 160 | g3 161 | (g5 162 | S'\xeb0+\xb78\xb1\xe8?' 163 | tRp48 164 | g3 165 | (g5 166 | S'@n\xd4\xc5\xe6\xa1\xe8?' 167 | tRp49 168 | g3 169 | (g5 170 | S'\xcet\xdaC\xf5\x08\xe9?' 171 | tRp50 172 | g3 173 | (g5 174 | S'\xcde\x8c|l|\xe7?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/cds_modification_comparisons/saved_data/model_performance.dict: -------------------------------------------------------------------------------- 1 | (dp1 2 | S'ep1_model' 3 | p2 4 | (cnumpy.core.multiarray 5 | scalar 6 | p3 7 | (cnumpy 8 | dtype 9 | p4 10 | (S'f8' 11 | I0 12 | I1 13 | tRp5 14 | (I3 15 | S'<' 16 | NNNI-1 17 | I-1 18 | I0 19 | tbS'=1\x11\x8b\xfc9\xe9?' 20 | tRp6 21 | g3 22 | (g5 23 | S'\xeel\x87ZL\x86\xe7?' 24 | tRp7 25 | g3 26 | (g5 27 | S' Dt\xb6\xa4\x8f\xe8?' 28 | tRp8 29 | g3 30 | (g5 31 | S'#b\x82R\xda\xba\xe8?' 32 | tRp9 33 | g3 34 | (g5 35 | S'\xb4\xdbw\x0b\xcb\x1b\xe8?' 36 | tRp10 37 | g3 38 | (g5 39 | S'\xf5\xe4Tjj\xd3\xe7?' 40 | tRp11 41 | tp12 42 | sS'e1_model' 43 | p13 44 | (g3 45 | (g5 46 | S'\xf2\x11\x85\x04\xa0*\xed?' 47 | tRp14 48 | g3 49 | (g5 50 | S'a\xdd\xf6\x1e\x05I\xeb?' 51 | tRp15 52 | g3 53 | (g5 54 | S'\xce9\xc8\x89\xab^\xe6?' 55 | tRp16 56 | g3 57 | (g5 58 | S'\xca\x85\xa8\x9f\x19V\xe7?' 59 | tRp17 60 | g3 61 | (g5 62 | S'\x8a\x05\xf6{t\xf9\xe5?' 63 | tRp18 64 | g3 65 | (g5 66 | S'\xb8N\x16\x85z-\xe8?' 67 | tRp19 68 | tp20 69 | sS'e2_model' 70 | p21 71 | (g3 72 | (g5 73 | S'\xce\xc5\x19\x91.<\xec?' 74 | tRp22 75 | g3 76 | (g5 77 | S'Pjk\xba\xce.\xec?' 78 | tRp23 79 | g3 80 | (g5 81 | S'\x17)\xaa\x17\xaa\n\xe6?' 82 | tRp24 83 | g3 84 | (g5 85 | S'B"vq\xa0\xd8\xe7?' 86 | tRp25 87 | g3 88 | (g5 89 | S'~\x85\xaaP\x07o\xe5?' 90 | tRp26 91 | g3 92 | (g5 93 | S'\xf1\r\x1e^V\x9f\xe8?' 94 | tRp27 95 | tp28 96 | sS'ep2_model' 97 | p29 98 | (g3 99 | (g5 100 | S'\x1c\xb8\xbej{\xdd\xe9?' 101 | tRp30 102 | g3 103 | (g5 104 | S'Y\xbf\xb0\xcbH(\xe9?' 105 | tRp31 106 | g3 107 | (g5 108 | S'm\xdf^HG\xef\xe8?' 109 | tRp32 110 | g3 111 | (g5 112 | S'%\x97\xef7\x8ft\xea?' 113 | tRp33 114 | g3 115 | (g5 116 | S'\xd2\x1c%XrV\xe8?' 117 | tRp34 118 | g3 119 | (g5 120 | S'\x8f[\x01\xbeX<\xe9?' 121 | tRp35 122 | tp36 123 | sS'emp2_model' 124 | p37 125 | (g3 126 | (g5 127 | S'9\x86\xfff\xab\xac\xe9?' 128 | tRp38 129 | g3 130 | (g5 131 | S'kX\x97\xfe#<\xe9?' 132 | tRp39 133 | g3 134 | (g5 135 | S't4H\t\xa3\x0b\xe7?' 136 | tRp40 137 | g3 138 | (g5 139 | S'P\xb0$\xc3\xe7b\xe8?' 140 | tRp41 141 | g3 142 | (g5 143 | S'\xcc\xfe\xee \x9cM\xe6?' 144 | tRp42 145 | g3 146 | (g5 147 | S'\xab[UeJ[\xe9?' 148 | tRp43 149 | tp44 150 | sS'emp1_model' 151 | p45 152 | (g3 153 | (g5 154 | S"\x04['\xd9\x06\xa0\xe8?" 155 | tRp46 156 | g3 157 | (g5 158 | S'\xc3\xcc\x87\xb9\xf1@\xe7?' 159 | tRp47 160 | g3 161 | (g5 162 | S'g\xd5\xe1\xc7\xddg\xe8?' 163 | tRp48 164 | g3 165 | (g5 166 | S'\xd1\xbd\x0f\x90\xd3\xb2\xe8?' 167 | tRp49 168 | g3 169 | (g5 170 | S'\xe0\xeap3\xae9\xe8?' 171 | tRp50 172 | g3 173 | (g5 174 | S'\x19\x94\x83\x080\xa4\xe7?' 175 | tRp51 176 | tp52 177 | s. -------------------------------------------------------------------------------- /modeling/saved_models/.ipynb_checkpoints/README-checkpoint: -------------------------------------------------------------------------------- 1 | 'Main' model for Figure 2: main_MRL_model.hdf5 2 | 3 | Retrained 'Main' model used in figure 4: retrained_main_MRL_model.hdf5 4 | 5 | The model used for the genetic algorithm: evolution_model.hdf5 6 | 7 | The retrained genetic algorithm model: retrained_evolution_model.hdf5 8 | 9 | The polysome distribution model from figure 2: polysome_profile_model.hdf5 -------------------------------------------------------------------------------- /modeling/saved_models/README: -------------------------------------------------------------------------------- 1 | 'Main' model for Figure 2: main_MRL_model.hdf5 2 | 3 | Retrained 'Main' model used in figure 4: retrained_main_MRL_model.hdf5 4 | 5 | The model used for the genetic algorithm: evolution_model.hdf5 6 | 7 | The retrained genetic algorithm model: retrained_evolution_model.hdf5 8 | 9 | The polysome distribution model from figure 2: polysome_profile_model.hdf5 -------------------------------------------------------------------------------- /modeling/saved_models/Varying_length_25to100_model.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/saved_models/Varying_length_25to100_model.hdf5 -------------------------------------------------------------------------------- /modeling/saved_models/evolution_model.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/saved_models/evolution_model.hdf5 -------------------------------------------------------------------------------- /modeling/saved_models/main_MRL_model.hdf5: 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-------------------------------------------------------------------------------- /modeling/saved_models/retrained_main_MRL_model.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pjsample/human_5utr_modeling/d53df410c7fb3fcd4bc4541bd7e8c6dc52b66fbe/modeling/saved_models/retrained_main_MRL_model.hdf5 -------------------------------------------------------------------------------- /sequence_design/.ipynb_checkpoints/evolve_for_target_MRL-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "import numpy as np\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "import scipy.stats as stats\n", 21 | "import seaborn as sns\n", 22 | "from sklearn import preprocessing\n", 23 | "\n", 24 | "%matplotlib inline\n", 25 | "pd.set_option(\"display.max_colwidth\",100)\n", 26 | "sns.set(style=\"ticks\", color_codes=True)\n", 27 | "\n", 28 | "def plot_data(data, x, y, x_title='x', y_title='y', xlim=None, ylim=None, size=4, alpha=0.02):\n", 29 | " slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n", 30 | " print '# of UTRs = ', len(data)\n", 31 | " print 'r-squared = ',r_value**2\n", 32 | "\n", 33 | " sns.set(style=\"ticks\", color_codes=True)\n", 34 | " g = sns.JointGrid(data=data, x=x, y=y, xlim=xlim, ylim=ylim, size=size)\n", 35 | " g = g.plot_joint(plt.scatter, color='#e01145', edgecolor=\"black\", alpha=alpha)\n", 36 | " f = g.fig\n", 37 | " f.text(x=0, y=0, s='r2 = {}'.format(round(r_value**2, 3)))\n", 38 | " g = g.plot_marginals(sns.distplot, kde=False, color='#e01145')\n", 39 | " g = g.set_axis_labels(x_title, y_title)\n", 40 | " \n", 41 | "import keras\n", 42 | "np.random.seed(1337)\n", 43 | "from keras.preprocessing import sequence\n", 44 | "from keras.optimizers import RMSprop\n", 45 | "from keras.models import Sequential\n", 46 | "from keras.layers.core import Dense\n", 47 | "from keras.layers.core import Dropout\n", 48 | "from keras.layers.core import Activation\n", 49 | "from keras.layers.core import Flatten\n", 50 | "from keras.layers.convolutional import Convolution1D\n", 51 | "from keras.constraints import maxnorm\n", 52 | "\n", 53 | "def one_hot_encode(df, col='utr', seq_len=50):\n", 54 | " # Dictionary returning one-hot encoding of nucleotides. \n", 55 | " nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n", 56 | " \n", 57 | " # Creat empty matrix.\n", 58 | " vectors=np.empty([len(df),seq_len,4])\n", 59 | " \n", 60 | " # Iterate through UTRs and one-hot encode\n", 61 | " for i,seq in enumerate(df[col].str[:seq_len]): \n", 62 | " seq = seq.lower()\n", 63 | " a = np.array([nuc_d[x] for x in seq])\n", 64 | " vectors[i] = a\n", 65 | " return vectors\n", 66 | "\n", 67 | "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n", 68 | " scaler = preprocessing.StandardScaler()\n", 69 | " scaler.fit(df[obs_col].reshape(-1,1))\n", 70 | " #df.loc[:,'obs_stab'] = test_df['stab_df']\n", 71 | " predictions = model.predict(test_seq).reshape(-1)\n", 72 | " df.loc[:,output_col] = scaler.inverse_transform(predictions)\n", 73 | " return df\n", 74 | "\n", 75 | "def binarize_sequences(df, col='utr', seq_len=54):\n", 76 | " vector=np.empty([len(df),seq_len,4])\n", 77 | " for i,seq in enumerate(df[col].str[:seq_len]):\n", 78 | " vector[i]=vectorizeSequence(seq.lower())\n", 79 | " return vector\n", 80 | "\n", 81 | "def vectorizeSequence(seq):\n", 82 | " # the order of the letters is not arbitrary.\n", 83 | " # Flip the matrix up-down and left-right for reverse compliment\n", 84 | " ltrdict = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n", 85 | " return np.array([ltrdict[x] for x in seq])" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "### Load data to get MRL values for scaling - convert the scaled predictions of the evolved sequences to MRL" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 3, 98 | "metadata": {}, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/plain": [ 103 | "StandardScaler(copy=True, with_mean=True, with_std=True)" 104 | ] 105 | }, 106 | "execution_count": 3, 107 | "metadata": {}, 108 | "output_type": "execute_result" 109 | } 110 | ], 111 | "source": [ 112 | "df = pd.read_csv('../data/egfp_unmod_1.csv')\n", 113 | "df.sort_values('total_reads', ascending=False).reset_index(drop=True)\n", 114 | "\n", 115 | "# Select a number of UTRs for the purpose of scaling.\n", 116 | "scale_utrs = df[:40000]\n", 117 | "\n", 118 | "# Scale\n", 119 | "scaler = preprocessing.StandardScaler()\n", 120 | "scaler.fit(scale_utrs['rl'].reshape(-1,1))" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "#### Choose model to use for evolving new sequences.\n", 128 | "In the paper, the model used for evolving sequences is saved here as \"evolution_model.hdf5\". This is an older model that predates the main egfp model used in the paper (trained on unmodified EGFP replicate #1). The main model used in the paper is labeled \"main_MRL_model.hdf5\". Both retrained models are also available." 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 4, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "from keras.models import load_model\n", 138 | "# model = load_model('../modeling/saved_models/evolution_model.hdf5')\n", 139 | "# model = load_model('../modeling/saved_models/retrained_evolution_model.hdf5')\n", 140 | "# model = load_model('../modeling/saved_models/main_MRL_model.hdf5')\n", 141 | "model = load_model('../modeling/saved_models/retrained_main_MRL_model.hdf5')" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "### Genetic algorithm" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 5, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "import random\n", 158 | "import math\n", 159 | "\n", 160 | "def ret_rand_nuc():\n", 161 | " x = random.randint(0,3)\n", 162 | " if x == 0:\n", 163 | " return [1,0,0,0] # A\n", 164 | " if x == 1:\n", 165 | " return [0,1,0,0] # C\n", 166 | " if x == 2:\n", 167 | " return [0,0,1,0] # G\n", 168 | " if x == 3:\n", 169 | " return [0,0,0,1] # T\n", 170 | " \n", 171 | "def vector_to_nuc(arr, seq_len=50):\n", 172 | " seq = ''\n", 173 | " for i in range(seq_len):\n", 174 | " if arr[i,0] == 1:\n", 175 | " seq = seq + 'A'\n", 176 | " if arr[i,1] == 1:\n", 177 | " seq = seq + 'C'\n", 178 | " if arr[i,2] == 1:\n", 179 | " seq = seq + 'G'\n", 180 | " if arr[i,3] == 1:\n", 181 | " seq = seq + 'T'\n", 182 | " return seq\n", 183 | "\n", 184 | "def convert_and_save(sequences, predictions):\n", 185 | " # Convert the one-hot encoded sequences to A, C, T, G\n", 186 | " seqs = []\n", 187 | " for nbr in xrange(len(sequences)):\n", 188 | " seqs.append(vector_to_nuc(sequences[nbr]))\n", 189 | " df = pd.DataFrame(data=[seqs,predictions.tolist()]).transpose()\n", 190 | " df.columns = ['utr', 'prediction']\n", 191 | " df.sort_values('prediction', ascending=False, inplace=True)\n", 192 | " return df\n", 193 | "\n", 194 | "def make_random_sequences(nbr_sequences, length, constant='', no_uaug=False, no_stop=False):\n", 195 | " # Make randomized sequences, allowing for the inclusion / exclusion of uATGs / stop codons\n", 196 | " seqs = []\n", 197 | " nucs = {0:'A', 1:'T', 2:'C', 3:'G'}\n", 198 | " i = 0\n", 199 | " while i < nbr_sequences:\n", 200 | " new_seq = ''\n", 201 | " for n in range(length - len(constant)):\n", 202 | " new_seq = new_seq + nucs[random.randint(0,3)]\n", 203 | " \n", 204 | " if no_uaug == False or (no_uaug==True and 'ATG' not in new_seq):\n", 205 | " if no_stop == False or (no_stop == True and ('TAG' not in new_seq and 'TGA' not in new_seq and 'TAA' not in new_seq)):\n", 206 | " new_seq = new_seq + constant\n", 207 | " seqs.append(new_seq)\n", 208 | " i+=1\n", 209 | " return seqs\n", 210 | "\n", 211 | "def simple_mutate(seq, nbr_bases=1, prob=1):\n", 212 | " if nbr_bases > 1 and prob > random.random():\n", 213 | " nbr_bases = nbr_bases\n", 214 | " else:\n", 215 | " nbr_bases = 1\n", 216 | " for i in range(nbr_bases):\n", 217 | " pos = random.randint(0, 49)\n", 218 | " seq[pos] = ret_rand_nuc()\n", 219 | " return seq\n", 220 | "\n", 221 | "def check_for_uaug(seq):\n", 222 | " seq = vector_to_nuc(seq)\n", 223 | " return 'ATG' in seq[:50]\n", 224 | "\n", 225 | "def check_for_stops(seq):\n", 226 | " seq = vector_to_nuc(seq)\n", 227 | " if 'TAG' in seq[:50] or 'TGA' in seq[:50] or 'TAA' in seq[:50]:\n", 228 | " return True\n", 229 | " return False\n", 230 | "\n", 231 | "def negative_selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1):\n", 232 | " seqs = np.empty([2,54,4])\n", 233 | " seqs[0] = seq.copy()\n", 234 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 235 | " \n", 236 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 237 | " return seqs[0]\n", 238 | " if no_stop == True and check_for_stops(seqs[1]):\n", 239 | " return seqs[0]\n", 240 | " \n", 241 | " scores = model.predict(seqs).reshape(-1)\n", 242 | " scores = scaler.inverse_transform(scores)\n", 243 | " if scores[1] < scores[0]:\n", 244 | " if scores[1] >= target_val:\n", 245 | " return seqs[1]\n", 246 | " else:\n", 247 | " return seqs[0]\n", 248 | " else:\n", 249 | " return seqs[0] \n", 250 | "\n", 251 | "def selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1):\n", 252 | " seqs = np.empty([2,50,4])\n", 253 | " seqs[0] = seq.copy()\n", 254 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 255 | " \n", 256 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 257 | " return seqs[0]\n", 258 | " if no_stop == True and check_for_stops(seqs[1]):\n", 259 | " return seqs[0]\n", 260 | " \n", 261 | " scores = model.predict(seqs).reshape(-1)\n", 262 | " scores = scaler.inverse_transform(scores)\n", 263 | " if scores[1] > scores[0]:\n", 264 | " if scores[1] <= target_val:\n", 265 | " return seqs[1]\n", 266 | " else:\n", 267 | " return seqs[0]\n", 268 | " else:\n", 269 | " return seqs[0] \n", 270 | " \n", 271 | "def selection_to_target(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1, seq_len=50, accept_range=0.1):\n", 272 | " seqs = np.empty([2,seq_len,4])\n", 273 | " # Save the incoming sequence before mutating\n", 274 | " seqs[0] = seq.copy()\n", 275 | " # The mutated sequence\n", 276 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 277 | " \n", 278 | " # Decide whether to continue with the new sequence based on the uAUG / stop codon preference\n", 279 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 280 | " return seqs[0]\n", 281 | " if no_stop == True and check_for_stops(seqs[1]):\n", 282 | " return seqs[0]\n", 283 | " \n", 284 | " scores = model.predict(seqs).reshape(-1)\n", 285 | " scores = scaler.inverse_transform(scores)\n", 286 | " \n", 287 | " # Accept sequences that fall within this range. May provide more sequence diversity\n", 288 | " if scores[0] >= target_val - accept_range and scores[0] <= target_val + accept_range:\n", 289 | " return seqs[0]\n", 290 | " else:\n", 291 | " if abs(target_val - scores[1]) <= abs(target_val - scores[0]):\n", 292 | " return seqs[1]\n", 293 | " else:\n", 294 | " return seqs[0] " 295 | ] 296 | }, 297 | { 298 | "cell_type": "markdown", 299 | "metadata": {}, 300 | "source": [ 301 | "_____" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "metadata": {}, 307 | "source": [ 308 | "### Evolve new sequences to hit target MRLs" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 6, 314 | "metadata": { 315 | "scrolled": false 316 | }, 317 | "outputs": [ 318 | { 319 | "name": "stdout", 320 | "output_type": "stream", 321 | "text": [ 322 | "Working on target_rl 5 with 10 sequences: 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 720 740 760 780 800\n", 323 | "Working on target_rl 8 with 10 sequences: 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 720 740 760 780 800\n" 324 | ] 325 | } 326 | ], 327 | "source": [ 328 | "# Dictionary where new sequences are saved\n", 329 | "evolved_seqs = {}\n", 330 | "\n", 331 | "# Number of evolution iterations\n", 332 | "iterations = 800\n", 333 | "# Number of bases to mutate if the probability to 'multi-mutate' is exceeded\n", 334 | "nbr_bases_to_mutate = 2\n", 335 | "# Probability to change multiple bases in an iteration\n", 336 | "prob_of_multi_mutation = 0.5\n", 337 | "# If using the original evolution model, set seq_len to 54. That model was\n", 338 | "# trained on UTRs that included the first for basees of the CDS (ATGG).\n", 339 | "seq_len = 50\n", 340 | "# Choose target MRLs and the number of sequences to create for each\n", 341 | "targets = [5, 8]\n", 342 | "seqs_per_target = [10, 10]\n", 343 | "# Choose whether or not to allow uAUGs and / or stop codons\n", 344 | "no_uaug = True\n", 345 | "no_stop = False\n", 346 | "\n", 347 | "for target_rl, nbr_sequences in zip(targets, seqs_per_target):\n", 348 | " print 'Working on target_rl {} with {} sequences:'.format(target_rl, nbr_sequences),\n", 349 | " \n", 350 | " # Randomly generate starting sequences for evolving\n", 351 | " rand_seqs = make_random_sequences(nbr_sequences, seq_len, no_uaug=no_uaug, no_stop=no_stop)\n", 352 | " test_sequences = np.empty([len(rand_seqs), seq_len, 4])\n", 353 | " i = 0\n", 354 | " \n", 355 | " # One-hot encode sequences\n", 356 | " for seq in rand_seqs:\n", 357 | " test_sequences[i] = vectorizeSequence(seq.lower())\n", 358 | " i += 1\n", 359 | " \n", 360 | " # Evolve sequences\n", 361 | " for generation in range(0, iterations):\n", 362 | " for i in range(len(test_sequences)):\n", 363 | " test_sequences[i] = selection_to_target(seq=test_sequences[i], model=model, scaler=scaler, target_val=target_rl,no_uaug=no_uaug,\n", 364 | " no_stop=no_stop, nbr_bases_to_mutate=nbr_bases_to_mutate, multi_mutate_prob=prob_of_multi_mutation, seq_len=seq_len)\n", 365 | "\n", 366 | " if (generation + 1) % 20 == 0:\n", 367 | " print generation + 1, \n", 368 | " print\n", 369 | " # Final prediction then convert to text sequence\n", 370 | " predictions = model.predict(test_sequences).reshape(-1)\n", 371 | " predictions = scaler.inverse_transform(predictions)\n", 372 | " converted_df = convert_and_save(test_sequences,predictions)\n", 373 | " \n", 374 | " evolved_seqs[target_rl] = converted_df" 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": 7, 380 | "metadata": {}, 381 | "outputs": [ 382 | { 383 | "name": "stderr", 384 | "output_type": "stream", 385 | "text": [ 386 | "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n", 387 | " (prop.get_family(), self.defaultFamily[fontext]))\n" 388 | ] 389 | }, 390 | { 391 | "data": { 392 | "image/png": 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393 | "text/plain": [ 394 | "" 395 | ] 396 | }, 397 | "metadata": {}, 398 | "output_type": "display_data" 399 | } 400 | ], 401 | "source": [ 402 | "for i in evolved_seqs:\n", 403 | " sns.kdeplot(evolved_seqs[i]['prediction'], shade=True, legend=True, label=i)" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 8, 409 | "metadata": {}, 410 | "outputs": [ 411 | { 412 | "data": { 413 | "text/html": [ 414 | "
\n", 415 | "\n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | "
utrprediction
4AAGTTGACGTAGAATTATACTTTCGGATCTTCATTGCACTAGACGTTGAT7.94126
0GAGAGAAAAAAGAGTTCAGAGTTAGTAGTGTACAGAGAGATCTGTTGGAT7.93827
9TTGGATCTCGTTTTATTTACTTGGAGTACAGAGTCGTATTACTAGATCCC7.9233
1GTGGAGTTTATCCAATCTACTTGGATTCCGAGTTCTTTGCAGGAAGAGCT7.91531
6ATCAATTGAGAGAGTCTTACAAATTCGATTTGGACGTCGACGCGAGGGTC7.91183
\n", 451 | "
" 452 | ], 453 | "text/plain": [ 454 | " utr prediction\n", 455 | "4 AAGTTGACGTAGAATTATACTTTCGGATCTTCATTGCACTAGACGTTGAT 7.94126\n", 456 | "0 GAGAGAAAAAAGAGTTCAGAGTTAGTAGTGTACAGAGAGATCTGTTGGAT 7.93827\n", 457 | "9 TTGGATCTCGTTTTATTTACTTGGAGTACAGAGTCGTATTACTAGATCCC 7.9233\n", 458 | "1 GTGGAGTTTATCCAATCTACTTGGATTCCGAGTTCTTTGCAGGAAGAGCT 7.91531\n", 459 | "6 ATCAATTGAGAGAGTCTTACAAATTCGATTTGGACGTCGACGCGAGGGTC 7.91183" 460 | ] 461 | }, 462 | "execution_count": 8, 463 | "metadata": {}, 464 | "output_type": "execute_result" 465 | } 466 | ], 467 | "source": [ 468 | "evolved_seqs[8].head()" 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": null, 474 | "metadata": {}, 475 | "outputs": [], 476 | "source": [] 477 | } 478 | ], 479 | "metadata": { 480 | "kernelspec": { 481 | "display_name": "Python 2", 482 | "language": "python", 483 | "name": "python2" 484 | }, 485 | "language_info": { 486 | "codemirror_mode": { 487 | "name": "ipython", 488 | "version": 2 489 | }, 490 | "file_extension": ".py", 491 | "mimetype": "text/x-python", 492 | "name": "python", 493 | "nbconvert_exporter": "python", 494 | "pygments_lexer": "ipython2", 495 | "version": "2.7.12" 496 | } 497 | }, 498 | "nbformat": 4, 499 | "nbformat_minor": 2 500 | } 501 | -------------------------------------------------------------------------------- /sequence_design/evolve_for_target_MRL.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "import numpy as np\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "import scipy.stats as stats\n", 21 | "import seaborn as sns\n", 22 | "from sklearn import preprocessing\n", 23 | "\n", 24 | "%matplotlib inline\n", 25 | "pd.set_option(\"display.max_colwidth\",100)\n", 26 | "sns.set(style=\"ticks\", color_codes=True)\n", 27 | "\n", 28 | "def plot_data(data, x, y, x_title='x', y_title='y', xlim=None, ylim=None, size=4, alpha=0.02):\n", 29 | " slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n", 30 | " print '# of UTRs = ', len(data)\n", 31 | " print 'r-squared = ',r_value**2\n", 32 | "\n", 33 | " sns.set(style=\"ticks\", color_codes=True)\n", 34 | " g = sns.JointGrid(data=data, x=x, y=y, xlim=xlim, ylim=ylim, size=size)\n", 35 | " g = g.plot_joint(plt.scatter, color='#e01145', edgecolor=\"black\", alpha=alpha)\n", 36 | " f = g.fig\n", 37 | " f.text(x=0, y=0, s='r2 = {}'.format(round(r_value**2, 3)))\n", 38 | " g = g.plot_marginals(sns.distplot, kde=False, color='#e01145')\n", 39 | " g = g.set_axis_labels(x_title, y_title)\n", 40 | " \n", 41 | "import keras\n", 42 | "np.random.seed(1337)\n", 43 | "from keras.preprocessing import sequence\n", 44 | "from keras.optimizers import RMSprop\n", 45 | "from keras.models import Sequential\n", 46 | "from keras.layers.core import Dense\n", 47 | "from keras.layers.core import Dropout\n", 48 | "from keras.layers.core import Activation\n", 49 | "from keras.layers.core import Flatten\n", 50 | "from keras.layers.convolutional import Convolution1D\n", 51 | "from keras.constraints import maxnorm\n", 52 | "\n", 53 | "def one_hot_encode(df, col='utr', seq_len=50):\n", 54 | " # Dictionary returning one-hot encoding of nucleotides. \n", 55 | " nuc_d = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n", 56 | " \n", 57 | " # Creat empty matrix.\n", 58 | " vectors=np.empty([len(df),seq_len,4])\n", 59 | " \n", 60 | " # Iterate through UTRs and one-hot encode\n", 61 | " for i,seq in enumerate(df[col].str[:seq_len]): \n", 62 | " seq = seq.lower()\n", 63 | " a = np.array([nuc_d[x] for x in seq])\n", 64 | " vectors[i] = a\n", 65 | " return vectors\n", 66 | "\n", 67 | "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n", 68 | " scaler = preprocessing.StandardScaler()\n", 69 | " scaler.fit(df[obs_col].reshape(-1,1))\n", 70 | " #df.loc[:,'obs_stab'] = test_df['stab_df']\n", 71 | " predictions = model.predict(test_seq).reshape(-1)\n", 72 | " df.loc[:,output_col] = scaler.inverse_transform(predictions)\n", 73 | " return df\n", 74 | "\n", 75 | "def binarize_sequences(df, col='utr', seq_len=54):\n", 76 | " vector=np.empty([len(df),seq_len,4])\n", 77 | " for i,seq in enumerate(df[col].str[:seq_len]):\n", 78 | " vector[i]=vectorizeSequence(seq.lower())\n", 79 | " return vector\n", 80 | "\n", 81 | "def vectorizeSequence(seq):\n", 82 | " # the order of the letters is not arbitrary.\n", 83 | " # Flip the matrix up-down and left-right for reverse compliment\n", 84 | " ltrdict = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n", 85 | " return np.array([ltrdict[x] for x in seq])" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "### Load data to get MRL values for scaling - convert the scaled predictions of the evolved sequences to MRL" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 3, 98 | "metadata": {}, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/plain": [ 103 | "StandardScaler(copy=True, with_mean=True, with_std=True)" 104 | ] 105 | }, 106 | "execution_count": 3, 107 | "metadata": {}, 108 | "output_type": "execute_result" 109 | } 110 | ], 111 | "source": [ 112 | "df = pd.read_csv('../data/egfp_unmod_1.csv')\n", 113 | "df.sort_values('total_reads', ascending=False).reset_index(drop=True)\n", 114 | "\n", 115 | "# Select a number of UTRs for the purpose of scaling.\n", 116 | "scale_utrs = df[:40000]\n", 117 | "\n", 118 | "# Scale\n", 119 | "scaler = preprocessing.StandardScaler()\n", 120 | "scaler.fit(scale_utrs['rl'].reshape(-1,1))" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "#### Choose model to use for evolving new sequences.\n", 128 | "In the paper, the model used for evolving sequences is saved here as \"evolution_model.hdf5\". This is an older model that predates the main egfp model used in the paper (trained on unmodified EGFP replicate #1). The main model used in the paper is labeled \"main_MRL_model.hdf5\". Both retrained models are also available." 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 4, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "from keras.models import load_model\n", 138 | "# model = load_model('../modeling/saved_models/evolution_model.hdf5')\n", 139 | "# model = load_model('../modeling/saved_models/retrained_evolution_model.hdf5')\n", 140 | "# model = load_model('../modeling/saved_models/main_MRL_model.hdf5')\n", 141 | "model = load_model('../modeling/saved_models/retrained_main_MRL_model.hdf5')" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "### Genetic algorithm" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 5, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "import random\n", 158 | "import math\n", 159 | "\n", 160 | "def ret_rand_nuc():\n", 161 | " x = random.randint(0,3)\n", 162 | " if x == 0:\n", 163 | " return [1,0,0,0] # A\n", 164 | " if x == 1:\n", 165 | " return [0,1,0,0] # C\n", 166 | " if x == 2:\n", 167 | " return [0,0,1,0] # G\n", 168 | " if x == 3:\n", 169 | " return [0,0,0,1] # T\n", 170 | " \n", 171 | "def vector_to_nuc(arr, seq_len=50):\n", 172 | " seq = ''\n", 173 | " for i in range(seq_len):\n", 174 | " if arr[i,0] == 1:\n", 175 | " seq = seq + 'A'\n", 176 | " if arr[i,1] == 1:\n", 177 | " seq = seq + 'C'\n", 178 | " if arr[i,2] == 1:\n", 179 | " seq = seq + 'G'\n", 180 | " if arr[i,3] == 1:\n", 181 | " seq = seq + 'T'\n", 182 | " return seq\n", 183 | "\n", 184 | "def convert_and_save(sequences, predictions):\n", 185 | " # Convert the one-hot encoded sequences to A, C, T, G\n", 186 | " seqs = []\n", 187 | " for nbr in xrange(len(sequences)):\n", 188 | " seqs.append(vector_to_nuc(sequences[nbr]))\n", 189 | " df = pd.DataFrame(data=[seqs,predictions.tolist()]).transpose()\n", 190 | " df.columns = ['utr', 'prediction']\n", 191 | " df.sort_values('prediction', ascending=False, inplace=True)\n", 192 | " return df\n", 193 | "\n", 194 | "def make_random_sequences(nbr_sequences, length, constant='', no_uaug=False, no_stop=False):\n", 195 | " # Make randomized sequences, allowing for the inclusion / exclusion of uATGs / stop codons\n", 196 | " seqs = []\n", 197 | " nucs = {0:'A', 1:'T', 2:'C', 3:'G'}\n", 198 | " i = 0\n", 199 | " while i < nbr_sequences:\n", 200 | " new_seq = ''\n", 201 | " for n in range(length - len(constant)):\n", 202 | " new_seq = new_seq + nucs[random.randint(0,3)]\n", 203 | " \n", 204 | " if no_uaug == False or (no_uaug==True and 'ATG' not in new_seq):\n", 205 | " if no_stop == False or (no_stop == True and ('TAG' not in new_seq and 'TGA' not in new_seq and 'TAA' not in new_seq)):\n", 206 | " new_seq = new_seq + constant\n", 207 | " seqs.append(new_seq)\n", 208 | " i+=1\n", 209 | " return seqs\n", 210 | "\n", 211 | "def simple_mutate(seq, nbr_bases=1, prob=1):\n", 212 | " if nbr_bases > 1 and prob > random.random():\n", 213 | " nbr_bases = nbr_bases\n", 214 | " else:\n", 215 | " nbr_bases = 1\n", 216 | " for i in range(nbr_bases):\n", 217 | " pos = random.randint(0, 49)\n", 218 | " seq[pos] = ret_rand_nuc()\n", 219 | " return seq\n", 220 | "\n", 221 | "def check_for_uaug(seq):\n", 222 | " seq = vector_to_nuc(seq)\n", 223 | " return 'ATG' in seq[:50]\n", 224 | "\n", 225 | "def check_for_stops(seq):\n", 226 | " seq = vector_to_nuc(seq)\n", 227 | " if 'TAG' in seq[:50] or 'TGA' in seq[:50] or 'TAA' in seq[:50]:\n", 228 | " return True\n", 229 | " return False\n", 230 | "\n", 231 | "def negative_selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1):\n", 232 | " seqs = np.empty([2,54,4])\n", 233 | " seqs[0] = seq.copy()\n", 234 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 235 | " \n", 236 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 237 | " return seqs[0]\n", 238 | " if no_stop == True and check_for_stops(seqs[1]):\n", 239 | " return seqs[0]\n", 240 | " \n", 241 | " scores = model.predict(seqs).reshape(-1)\n", 242 | " scores = scaler.inverse_transform(scores)\n", 243 | " if scores[1] < scores[0]:\n", 244 | " if scores[1] >= target_val:\n", 245 | " return seqs[1]\n", 246 | " else:\n", 247 | " return seqs[0]\n", 248 | " else:\n", 249 | " return seqs[0] \n", 250 | "\n", 251 | "def selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1):\n", 252 | " seqs = np.empty([2,50,4])\n", 253 | " seqs[0] = seq.copy()\n", 254 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 255 | " \n", 256 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 257 | " return seqs[0]\n", 258 | " if no_stop == True and check_for_stops(seqs[1]):\n", 259 | " return seqs[0]\n", 260 | " \n", 261 | " scores = model.predict(seqs).reshape(-1)\n", 262 | " scores = scaler.inverse_transform(scores)\n", 263 | " if scores[1] > scores[0]:\n", 264 | " if scores[1] <= target_val:\n", 265 | " return seqs[1]\n", 266 | " else:\n", 267 | " return seqs[0]\n", 268 | " else:\n", 269 | " return seqs[0] \n", 270 | " \n", 271 | "def selection_to_target(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1, seq_len=50, accept_range=0.1):\n", 272 | " seqs = np.empty([2,seq_len,4])\n", 273 | " # Save the incoming sequence before mutating\n", 274 | " seqs[0] = seq.copy()\n", 275 | " # The mutated sequence\n", 276 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 277 | " \n", 278 | " # Decide whether to continue with the new sequence based on the uAUG / stop codon preference\n", 279 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 280 | " return seqs[0]\n", 281 | " if no_stop == True and check_for_stops(seqs[1]):\n", 282 | " return seqs[0]\n", 283 | " \n", 284 | " scores = model.predict(seqs).reshape(-1)\n", 285 | " scores = scaler.inverse_transform(scores)\n", 286 | " \n", 287 | " # Accept sequences that fall within this range. May provide more sequence diversity\n", 288 | " if scores[0] >= target_val - accept_range and scores[0] <= target_val + accept_range:\n", 289 | " return seqs[0]\n", 290 | " else:\n", 291 | " if abs(target_val - scores[1]) <= abs(target_val - scores[0]):\n", 292 | " return seqs[1]\n", 293 | " else:\n", 294 | " return seqs[0] " 295 | ] 296 | }, 297 | { 298 | "cell_type": "markdown", 299 | "metadata": {}, 300 | "source": [ 301 | "_____" 302 | ] 303 | }, 304 | { 305 | "cell_type": "markdown", 306 | "metadata": {}, 307 | "source": [ 308 | "### Evolve new sequences to hit target MRLs" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 6, 314 | "metadata": { 315 | "scrolled": false 316 | }, 317 | "outputs": [ 318 | { 319 | "name": "stdout", 320 | "output_type": "stream", 321 | "text": [ 322 | "Working on target_rl 5 with 10 sequences: 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 720 740 760 780 800\n", 323 | "Working on target_rl 8 with 10 sequences: 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 720 740 760 780 800\n" 324 | ] 325 | } 326 | ], 327 | "source": [ 328 | "# Dictionary where new sequences are saved\n", 329 | "evolved_seqs = {}\n", 330 | "\n", 331 | "# Number of evolution iterations\n", 332 | "iterations = 800\n", 333 | "# Number of bases to mutate if the probability to 'multi-mutate' is exceeded\n", 334 | "nbr_bases_to_mutate = 2\n", 335 | "# Probability to change multiple bases in an iteration\n", 336 | "prob_of_multi_mutation = 0.5\n", 337 | "# If using the original evolution model, set seq_len to 54. That model was\n", 338 | "# trained on UTRs that included the first for basees of the CDS (ATGG).\n", 339 | "seq_len = 50\n", 340 | "# Choose target MRLs and the number of sequences to create for each\n", 341 | "targets = [5, 8]\n", 342 | "seqs_per_target = [10, 10]\n", 343 | "# Choose whether or not to allow uAUGs and / or stop codons\n", 344 | "no_uaug = True\n", 345 | "no_stop = False\n", 346 | "\n", 347 | "for target_rl, nbr_sequences in zip(targets, seqs_per_target):\n", 348 | " print 'Working on target_rl {} with {} sequences:'.format(target_rl, nbr_sequences),\n", 349 | " \n", 350 | " # Randomly generate starting sequences for evolving\n", 351 | " rand_seqs = make_random_sequences(nbr_sequences, seq_len, no_uaug=no_uaug, no_stop=no_stop)\n", 352 | " test_sequences = np.empty([len(rand_seqs), seq_len, 4])\n", 353 | " i = 0\n", 354 | " \n", 355 | " # One-hot encode sequences\n", 356 | " for seq in rand_seqs:\n", 357 | " test_sequences[i] = vectorizeSequence(seq.lower())\n", 358 | " i += 1\n", 359 | " \n", 360 | " # Evolve sequences\n", 361 | " for generation in range(0, iterations):\n", 362 | " for i in range(len(test_sequences)):\n", 363 | " test_sequences[i] = selection_to_target(seq=test_sequences[i], model=model, scaler=scaler, target_val=target_rl,no_uaug=no_uaug,\n", 364 | " no_stop=no_stop, nbr_bases_to_mutate=nbr_bases_to_mutate, multi_mutate_prob=prob_of_multi_mutation, seq_len=seq_len)\n", 365 | "\n", 366 | " if (generation + 1) % 20 == 0:\n", 367 | " print generation + 1, \n", 368 | " print\n", 369 | " # Final prediction then convert to text sequence\n", 370 | " predictions = model.predict(test_sequences).reshape(-1)\n", 371 | " predictions = scaler.inverse_transform(predictions)\n", 372 | " converted_df = convert_and_save(test_sequences,predictions)\n", 373 | " \n", 374 | " evolved_seqs[target_rl] = converted_df" 375 | ] 376 | }, 377 | { 378 | "cell_type": "code", 379 | "execution_count": 7, 380 | "metadata": {}, 381 | "outputs": [ 382 | { 383 | "name": "stderr", 384 | "output_type": "stream", 385 | "text": [ 386 | "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n", 387 | " (prop.get_family(), self.defaultFamily[fontext]))\n" 388 | ] 389 | }, 390 | { 391 | "data": { 392 | "image/png": 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393 | "text/plain": [ 394 | "" 395 | ] 396 | }, 397 | "metadata": {}, 398 | "output_type": "display_data" 399 | } 400 | ], 401 | "source": [ 402 | "for i in evolved_seqs:\n", 403 | " sns.kdeplot(evolved_seqs[i]['prediction'], shade=True, legend=True, label=i)" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 8, 409 | "metadata": {}, 410 | "outputs": [ 411 | { 412 | "data": { 413 | "text/html": [ 414 | "
\n", 415 | "\n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | "
utrprediction
4AAGTTGACGTAGAATTATACTTTCGGATCTTCATTGCACTAGACGTTGAT7.94126
0GAGAGAAAAAAGAGTTCAGAGTTAGTAGTGTACAGAGAGATCTGTTGGAT7.93827
9TTGGATCTCGTTTTATTTACTTGGAGTACAGAGTCGTATTACTAGATCCC7.9233
1GTGGAGTTTATCCAATCTACTTGGATTCCGAGTTCTTTGCAGGAAGAGCT7.91531
6ATCAATTGAGAGAGTCTTACAAATTCGATTTGGACGTCGACGCGAGGGTC7.91183
\n", 451 | "
" 452 | ], 453 | "text/plain": [ 454 | " utr prediction\n", 455 | "4 AAGTTGACGTAGAATTATACTTTCGGATCTTCATTGCACTAGACGTTGAT 7.94126\n", 456 | "0 GAGAGAAAAAAGAGTTCAGAGTTAGTAGTGTACAGAGAGATCTGTTGGAT 7.93827\n", 457 | "9 TTGGATCTCGTTTTATTTACTTGGAGTACAGAGTCGTATTACTAGATCCC 7.9233\n", 458 | "1 GTGGAGTTTATCCAATCTACTTGGATTCCGAGTTCTTTGCAGGAAGAGCT 7.91531\n", 459 | "6 ATCAATTGAGAGAGTCTTACAAATTCGATTTGGACGTCGACGCGAGGGTC 7.91183" 460 | ] 461 | }, 462 | "execution_count": 8, 463 | "metadata": {}, 464 | "output_type": "execute_result" 465 | } 466 | ], 467 | "source": [ 468 | "evolved_seqs[8].head()" 469 | ] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "execution_count": null, 474 | "metadata": {}, 475 | "outputs": [], 476 | "source": [] 477 | } 478 | ], 479 | "metadata": { 480 | "kernelspec": { 481 | "display_name": "Python 2", 482 | "language": "python", 483 | "name": "python2" 484 | }, 485 | "language_info": { 486 | "codemirror_mode": { 487 | "name": "ipython", 488 | "version": 2 489 | }, 490 | "file_extension": ".py", 491 | "mimetype": "text/x-python", 492 | "name": "python", 493 | "nbconvert_exporter": "python", 494 | "pygments_lexer": "ipython2", 495 | "version": "2.7.12" 496 | } 497 | }, 498 | "nbformat": 4, 499 | "nbformat_minor": 2 500 | } 501 | -------------------------------------------------------------------------------- /sequence_design/stepwise_evolve_utrs.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", 13 | " \"This module will be removed in 0.20.\", DeprecationWarning)\n", 14 | "Using TensorFlow backend.\n" 15 | ] 16 | } 17 | ], 18 | "source": [ 19 | "import pandas as pd\n", 20 | "import numpy as np\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "import scipy.stats as stats\n", 23 | "import seaborn as sns\n", 24 | "from sklearn import preprocessing\n", 25 | "from sklearn.cross_validation import train_test_split\n", 26 | "\n", 27 | "%matplotlib inline\n", 28 | "pd.set_option(\"display.max_colwidth\",100)\n", 29 | "sns.set(style=\"ticks\", color_codes=True)\n", 30 | "def plot_data(data, x, y, x_title='x', y_title='y', xlim=None, ylim=None, size=4, alpha=0.02):\n", 31 | " slope, intercept, r_value, p_value, std_err = stats.linregress(data[x],data[y])\n", 32 | " print '# of UTRs = ', len(data)\n", 33 | " print 'r-squared = ',r_value**2\n", 34 | "\n", 35 | " sns.set(style=\"ticks\", color_codes=True)\n", 36 | " g = sns.JointGrid(data=data, x=x, y=y, xlim=xlim, ylim=ylim, size=size)\n", 37 | " g = g.plot_joint(plt.scatter, color='#e01145', edgecolor=\"black\", alpha=alpha)\n", 38 | " f = g.fig\n", 39 | " f.text(x=0, y=0, s='r2 = {}'.format(round(r_value**2, 3)))\n", 40 | " g = g.plot_marginals(sns.distplot, kde=False, color='#e01145')\n", 41 | " g = g.set_axis_labels(x_title, y_title)\n", 42 | " \n", 43 | "def vectorizeSequence(seq):\n", 44 | " # the order of the letters is not arbitrary.\n", 45 | " # Flip the matrix up-down and left-right for reverse compliment\n", 46 | " ltrdict = {'a':[1,0,0,0],'c':[0,1,0,0],'g':[0,0,1,0],'t':[0,0,0,1], 'n':[0,0,0,0]}\n", 47 | " return np.array([ltrdict[x] for x in seq])\n", 48 | "\n", 49 | "import keras\n", 50 | "np.random.seed(1337)\n", 51 | "\n", 52 | "from keras.preprocessing import sequence\n", 53 | "from keras.optimizers import RMSprop\n", 54 | "from keras.models import Sequential\n", 55 | "from keras.layers.core import Dense\n", 56 | "from keras.layers.core import Dropout\n", 57 | "from keras.layers.core import Activation\n", 58 | "from keras.layers.core import Flatten\n", 59 | "from keras.layers.convolutional import Convolution1D, MaxPooling1D\n", 60 | "from keras.constraints import maxnorm\n", 61 | "from keras.callbacks import ModelCheckpoint, EarlyStopping\n", 62 | "\n", 63 | "def test_data(df, model, test_seq, obs_col, output_col='pred'):\n", 64 | " scaler = preprocessing.StandardScaler()\n", 65 | " scaler.fit(df[obs_col].reshape(-1,1))\n", 66 | " #df.loc[:,'obs_stab'] = test_df['stab_df']\n", 67 | " predictions = model.predict(test_seq).reshape(-1)\n", 68 | " df.loc[:,output_col] = scaler.inverse_transform(predictions)\n", 69 | " return df\n", 70 | "\n", 71 | "def binarize_sequences(df, col='utr', seq_len=54):\n", 72 | " vector=np.empty([len(df),seq_len,4])\n", 73 | " for i,seq in enumerate(df[col].str[:seq_len]):\n", 74 | " vector[i]=vectorizeSequence(seq.lower())\n", 75 | " return vector" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "### Load data to get MRL values for scaling - convert the scaled predictions of the evolved sequences to MRL" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 2, 88 | "metadata": {}, 89 | "outputs": [ 90 | { 91 | "data": { 92 | "text/plain": [ 93 | "StandardScaler(copy=True, with_mean=True, with_std=True)" 94 | ] 95 | }, 96 | "execution_count": 2, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | } 100 | ], 101 | "source": [ 102 | "df = pd.read_csv('../data/egfp_unmod_1.csv')\n", 103 | "df.sort_values('total_reads', ascending=False).reset_index(drop=True)\n", 104 | "\n", 105 | "# Select a number of UTRs for the purpose of scaling.\n", 106 | "scale_utrs = df[:40000]\n", 107 | "\n", 108 | "# Scale\n", 109 | "scaler = preprocessing.StandardScaler()\n", 110 | "scaler.fit(scale_utrs['rl'].reshape(-1,1))" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "#### Choose model to use for evolving new sequences.\n", 118 | "In the paper, the model used for evolving sequences is saved here as \"evolution_model.hdf5\". This is an older model that predates the main egfp model used in the paper (trained on unmodified EGFP replicate #1). The main model used in the paper is labeled \"main_MRL_model.hdf5\". Both retrained models are also available." 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 3, 124 | "metadata": {}, 125 | "outputs": [], 126 | "source": [ 127 | "from keras.models import load_model\n", 128 | "# model = load_model('../modeling/saved_models/evolution_model.hdf5')\n", 129 | "# model = load_model('../modeling/saved_models/retrained_evolution_model.hdf5')\n", 130 | "# model = load_model('../modeling/saved_models/main_MRL_model.hdf5')\n", 131 | "model = load_model('../modeling/saved_models/retrained_main_MRL_model.hdf5')" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "### Genetic algorithm" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 4, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "import random\n", 148 | "import math\n", 149 | "\n", 150 | "def ret_rand_nuc():\n", 151 | " x = random.randint(0,3)\n", 152 | " if x == 0:\n", 153 | " return [1,0,0,0] # A\n", 154 | " if x == 1:\n", 155 | " return [0,1,0,0] # C\n", 156 | " if x == 2:\n", 157 | " return [0,0,1,0] # G\n", 158 | " if x == 3:\n", 159 | " return [0,0,0,1] # T\n", 160 | " \n", 161 | "def vector_to_nuc(arr, seq_len=50):\n", 162 | " seq = ''\n", 163 | " for i in range(seq_len):\n", 164 | " if arr[i,0] == 1:\n", 165 | " seq = seq + 'A'\n", 166 | " if arr[i,1] == 1:\n", 167 | " seq = seq + 'C'\n", 168 | " if arr[i,2] == 1:\n", 169 | " seq = seq + 'G'\n", 170 | " if arr[i,3] == 1:\n", 171 | " seq = seq + 'T'\n", 172 | " return seq\n", 173 | "\n", 174 | "def convert_and_save(sequences, predictions):\n", 175 | " # Convert the one-hot encoded sequences to A, C, T, G\n", 176 | " seqs = []\n", 177 | " for nbr in xrange(len(sequences)):\n", 178 | " seqs.append(vector_to_nuc(sequences[nbr]))\n", 179 | " df = pd.DataFrame(data=[seqs,predictions.tolist()]).transpose()\n", 180 | " df.columns = ['utr', 'prediction']\n", 181 | " df.sort_values('prediction', ascending=False, inplace=True)\n", 182 | " return df\n", 183 | "\n", 184 | "def make_random_sequences(nbr_sequences, length, constant='', no_uaug=False, no_stop=False):\n", 185 | " # Make randomize sequences, allowing for the inclusion / exclusion of uATGs / stop codons\n", 186 | " seqs = []\n", 187 | " nucs = {0:'A', 1:'T', 2:'C', 3:'G'}\n", 188 | " i = 0\n", 189 | " while i < nbr_sequences:\n", 190 | " new_seq = ''\n", 191 | " for n in range(length - len(constant)):\n", 192 | " new_seq = new_seq + nucs[random.randint(0,3)]\n", 193 | " \n", 194 | " if no_uaug == False or (no_uaug==True and 'ATG' not in new_seq):\n", 195 | " if no_stop == False or (no_stop == True and ('TAG' not in new_seq and 'TGA' not in new_seq and 'TAA' not in new_seq)):\n", 196 | " new_seq = new_seq + constant\n", 197 | " seqs.append(new_seq)\n", 198 | " i+=1\n", 199 | " return seqs\n", 200 | "\n", 201 | "def simple_mutate(seq, nbr_bases=1, prob=1):\n", 202 | " if nbr_bases > 1 and prob > random.random():\n", 203 | " nbr_bases = nbr_bases\n", 204 | " else:\n", 205 | " nbr_bases = 1\n", 206 | " for i in range(nbr_bases):\n", 207 | " pos = random.randint(0, 49)\n", 208 | " seq[pos] = ret_rand_nuc()\n", 209 | " return seq\n", 210 | "\n", 211 | "def check_for_uaug(seq):\n", 212 | " seq = vector_to_nuc(seq)\n", 213 | " return 'ATG' in seq[:50]\n", 214 | "\n", 215 | "def check_for_stops(seq):\n", 216 | " seq = vector_to_nuc(seq)\n", 217 | " if 'TAG' in seq[:50] or 'TGA' in seq[:50] or 'TAA' in seq[:50]:\n", 218 | " return True\n", 219 | " return False\n", 220 | "\n", 221 | "def negative_selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1, seq_len=50):\n", 222 | " seqs = np.empty([2,seq_len,4])\n", 223 | " seqs[0] = seq.copy()\n", 224 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 225 | " \n", 226 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 227 | " return seqs[0]\n", 228 | " if no_stop == True and check_for_stops(seqs[1]):\n", 229 | " return seqs[0]\n", 230 | " \n", 231 | " scores = model.predict(seqs).reshape(-1)\n", 232 | " scores = scaler.inverse_transform(scores)\n", 233 | " if scores[1] < scores[0]:\n", 234 | " if scores[1] >= target_val:\n", 235 | " return seqs[1]\n", 236 | " else:\n", 237 | " return seqs[0]\n", 238 | " else:\n", 239 | " return seqs[0] \n", 240 | "\n", 241 | "def selection(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1, seq_len=50):\n", 242 | " seqs = np.empty([2, seq_len, 4])\n", 243 | " seqs[0] = seq.copy()\n", 244 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 245 | " \n", 246 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 247 | " return seqs[0]\n", 248 | " if no_stop == True and check_for_stops(seqs[1]):\n", 249 | " return seqs[0]\n", 250 | " \n", 251 | " scores = model.predict(seqs).reshape(-1)\n", 252 | " scores = scaler.inverse_transform(scores)\n", 253 | " if scores[1] > scores[0]:\n", 254 | " if scores[1] <= target_val:\n", 255 | " return seqs[1]\n", 256 | " else:\n", 257 | " return seqs[0]\n", 258 | " else:\n", 259 | " return seqs[0] \n", 260 | " \n", 261 | "def selection_to_target(seq, model, scaler, target_val, no_uaug=False, no_stop=False, nbr_bases_to_mutate=1, multi_mutate_prob=1, seq_len=50, accept_range=0.1):\n", 262 | " seqs = np.empty([2,seq_len,4])\n", 263 | " # Save the incoming sequence before mutating\n", 264 | " seqs[0] = seq.copy()\n", 265 | " # The mutated sequence\n", 266 | " seqs[1] = simple_mutate(seq.copy(), nbr_bases=nbr_bases_to_mutate, prob=multi_mutate_prob)\n", 267 | " \n", 268 | " # Decide whether to continue with the new sequence based on the uAUG / stop codon preference\n", 269 | " if no_uaug == True and check_for_uaug(seqs[1]):\n", 270 | " return seqs[0]\n", 271 | " if no_stop == True and check_for_stops(seqs[1]):\n", 272 | " return seqs[0]\n", 273 | " \n", 274 | " scores = model.predict(seqs).reshape(-1)\n", 275 | " scores = scaler.inverse_transform(scores)\n", 276 | " \n", 277 | " # Accept sequences that fall within this range. May provide more sequence diversity\n", 278 | " if scores[0] >= target_val - accept_range and scores[0] <= target_val + accept_range:\n", 279 | " return seqs[0]\n", 280 | " else:\n", 281 | " if abs(target_val - scores[1]) <= abs(target_val - scores[0]):\n", 282 | " return seqs[1]\n", 283 | " else:\n", 284 | " return seqs[0] " 285 | ] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "metadata": {}, 290 | "source": [ 291 | "_____" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "### Evolve new sequences - random to worst to best" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "execution_count": 5, 304 | "metadata": {}, 305 | "outputs": [ 306 | { 307 | "name": "stdout", 308 | "output_type": "stream", 309 | "text": [ 310 | "0 100\n" 311 | ] 312 | } 313 | ], 314 | "source": [ 315 | "# Dictionary where new sequences are saved\n", 316 | "evolved_seqs = {}\n", 317 | "# Number of evolution iterations\n", 318 | "iterations = 100\n", 319 | "# Number of bases to mutate if the probability to 'multi-mutate' is exceeded\n", 320 | "nbr_bases_to_mutate = 2\n", 321 | "# Probability to change multiple bases in an iteration\n", 322 | "prob_of_multi_mutation = 0.5\n", 323 | "# If using the original evolution model, set seq_len to 54. That model was\n", 324 | "# trained on UTRs that included the first for basees of the CDS (ATGG).\n", 325 | "seq_len = 50\n", 326 | "# Choose whether or not to allow uAUGs and / or stop codons\n", 327 | "no_uaug = True\n", 328 | "no_stop = False\n", 329 | "\n", 330 | "nbr_sequences = 5\n", 331 | "rand_seqs = make_random_sequences(nbr_sequences, seq_len, no_uaug=True, no_stop=True)\n", 332 | "e_seqs = np.empty([len(rand_seqs), seq_len, 4])\n", 333 | "\n", 334 | "i = 0\n", 335 | "for seq in rand_seqs:\n", 336 | " e_seqs[i]=vectorizeSequence(seq.lower())\n", 337 | " i += 1\n", 338 | "\n", 339 | "for x in range(nbr_sequences):\n", 340 | " evolved_seqs[x] = np.empty((iterations*2,2), dtype=object)\n", 341 | "\n", 342 | "for gen in range(0, iterations*2):\n", 343 | " for i in range(len(e_seqs)):\n", 344 | " if gen < iterations:\n", 345 | " # Evolve for lowest MRL\n", 346 | " target_rl = 0\n", 347 | " e_seqs[i] = negative_selection(seq=e_seqs[i], model=model, scaler=scaler, target_val=target_rl, no_uaug=no_uaug,\n", 348 | " no_stop=no_stop, nbr_bases_to_mutate=nbr_bases_to_mutate, multi_mutate_prob=prob_of_multi_mutation, seq_len=seq_len)\n", 349 | " else:\n", 350 | " # Evolve to highest MRL - set target_rl to arbitrarily high value\n", 351 | " target_rl = 20\n", 352 | " e_seqs[i] = selection(seq=e_seqs[i], model=model, scaler=scaler, target_val=target_rl,no_uaug=no_uaug,\n", 353 | " no_stop=no_stop, nbr_bases_to_mutate=nbr_bases_to_mutate, multi_mutate_prob=prob_of_multi_mutation, seq_len=seq_len)\n", 354 | " \n", 355 | " for x in range(nbr_sequences):\n", 356 | " evolved_seqs[x][gen, 0] = vector_to_nuc(e_seqs[x])\n", 357 | " evolved_seqs[x][gen, 1] = model.predict(e_seqs).reshape(-1)[x]\n", 358 | " \n", 359 | " if gen % 100 == 0:\n", 360 | " print gen,\n", 361 | "\n", 362 | "for x in range(nbr_sequences):\n", 363 | " evolved_seqs[x][:,1] = scaler.inverse_transform(evolved_seqs[x][:, 1])" 364 | ] 365 | }, 366 | { 367 | "cell_type": "markdown", 368 | "metadata": {}, 369 | "source": [ 370 | "### Plot" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 6, 376 | "metadata": {}, 377 | "outputs": [ 378 | { 379 | "name": "stderr", 380 | "output_type": "stream", 381 | "text": [ 382 | "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n", 383 | " (prop.get_family(), self.defaultFamily[fontext]))\n" 384 | ] 385 | }, 386 | { 387 | "data": { 388 | "image/png": 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HzTffzN13301zc3N/2NYrjOY2QXGRFKna1p04Sqfj6Y3MzFVjKPvrT91YqHH6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\nHMKBtwIcDgV+Oj/nGs5yUNYjtZpQvvjiC3bv3s3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389 | "text/plain": [ 390 | "" 391 | ] 392 | }, 393 | "metadata": {}, 394 | "output_type": "display_data" 395 | } 396 | ], 397 | "source": [ 398 | "for x in range(nbr_sequences)[:5]:\n", 399 | " plt.plot(range(evolved_seqs[0].shape[0]), evolved_seqs[x][:,1])\n", 400 | "plt.xlabel('Generations')\n", 401 | "plt.ylabel('Predicted mean ribosome load')\n", 402 | "plt.gcf().set_size_inches((4,4))" 403 | ] 404 | }, 405 | { 406 | "cell_type": "markdown", 407 | "metadata": {}, 408 | "source": [ 409 | "##### Now get rid of steps where the sequence didn't change, make a dataframe and store in a dictionary." 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 7, 415 | "metadata": {}, 416 | "outputs": [ 417 | { 418 | "name": "stdout", 419 | "output_type": "stream", 420 | "text": [ 421 | "272\n" 422 | ] 423 | } 424 | ], 425 | "source": [ 426 | "wb_seqs = {}\n", 427 | "count = 0\n", 428 | "for x in range(len(evolved_seqs)):\n", 429 | " tmp = pd.DataFrame(evolved_seqs[x], columns=['seq', 'pred_rl'])\n", 430 | " tmp['generation'] = tmp.index\n", 431 | " tmp = tmp.drop_duplicates(subset=['seq'])\n", 432 | " tmp.reset_index(inplace=True, drop=True)\n", 433 | " wb_seqs[x] = tmp\n", 434 | " count += len(tmp)\n", 435 | "print count" 436 | ] 437 | }, 438 | { 439 | "cell_type": "markdown", 440 | "metadata": {}, 441 | "source": [ 442 | "________________" 443 | ] 444 | }, 445 | { 446 | "cell_type": "markdown", 447 | "metadata": {}, 448 | "source": [ 449 | "### Evolving random to best" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": 10, 455 | "metadata": { 456 | "scrolled": true 457 | }, 458 | "outputs": [ 459 | { 460 | "name": "stderr", 461 | "output_type": "stream", 462 | "text": [ 463 | "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", 464 | " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" 465 | ] 466 | }, 467 | { 468 | "data": { 469 | "text/plain": [ 470 | "StandardScaler(copy=True, with_mean=True, with_std=True)" 471 | ] 472 | }, 473 | "execution_count": 10, 474 | "metadata": {}, 475 | "output_type": "execute_result" 476 | } 477 | ], 478 | "source": [ 479 | "nbr_sequences = 20\n", 480 | "rand_seqs = make_random_sequences(nbr_sequences, 54, 'ATGG', no_uaug=True, no_stop=True)\n", 481 | "test_sequences = np.empty([len(rand_seqs),54,4])\n", 482 | "\n", 483 | "i = 0\n", 484 | "for seq in rand_seqs:\n", 485 | " test_sequences[i]=vectorizeSequence(seq.lower())\n", 486 | " i += 1\n", 487 | "\n", 488 | "scaler = preprocessing.StandardScaler()\n", 489 | "scaler.fit(scale_utrs['rl'])" 490 | ] 491 | }, 492 | { 493 | "cell_type": "code", 494 | "execution_count": 13, 495 | "metadata": {}, 496 | "outputs": [ 497 | { 498 | "name": "stdout", 499 | "output_type": "stream", 500 | "text": [ 501 | "0\n" 502 | ] 503 | } 504 | ], 505 | "source": [ 506 | "# Dictionary where new sequences are saved\n", 507 | "evolved_seqs = {}\n", 508 | "# Number of evolution iterations\n", 509 | "iterations = 100\n", 510 | "# Number of bases to mutate if the probability to 'multi-mutate' is exceeded\n", 511 | "nbr_bases_to_mutate = 2\n", 512 | "# Probability to change multiple bases in an iteration\n", 513 | "prob_of_multi_mutation = 0.5\n", 514 | "# If using the original evolution model, set seq_len to 54. That model was\n", 515 | "# trained on UTRs that included the first for basees of the CDS (ATGG).\n", 516 | "seq_len = 50\n", 517 | "# Choose whether or not to allow uAUGs and / or stop codons\n", 518 | "no_uaug = True\n", 519 | "no_stop = False\n", 520 | "\n", 521 | "nbr_sequences = 5\n", 522 | "rand_seqs = make_random_sequences(nbr_sequences, seq_len, no_uaug=True, no_stop=True)\n", 523 | "e_seqs = np.empty([len(rand_seqs), seq_len, 4])\n", 524 | "\n", 525 | "i = 0\n", 526 | "for seq in rand_seqs:\n", 527 | " e_seqs[i]=vectorizeSequence(seq.lower())\n", 528 | " i += 1\n", 529 | "\n", 530 | "for x in range(nbr_sequences):\n", 531 | " evolved_seqs[x] = np.empty((iterations,2), dtype=object)\n", 532 | "\n", 533 | "for gen in range(0, iterations):\n", 534 | " for i in range(len(e_seqs)):\n", 535 | " # Evolve to highest MRL - set target_rl to arbitrarily high value\n", 536 | " target_rl = 20\n", 537 | " e_seqs[i] = selection(seq=e_seqs[i], model=model, scaler=scaler, target_val=target_rl,no_uaug=no_uaug,\n", 538 | " no_stop=no_stop, nbr_bases_to_mutate=nbr_bases_to_mutate, multi_mutate_prob=prob_of_multi_mutation, seq_len=seq_len)\n", 539 | " \n", 540 | " for x in range(nbr_sequences):\n", 541 | " evolved_seqs[x][gen, 0] = vector_to_nuc(e_seqs[x])\n", 542 | " evolved_seqs[x][gen, 1] = model.predict(e_seqs).reshape(-1)[x]\n", 543 | " \n", 544 | " if gen % 100 == 0:\n", 545 | " print gen,\n", 546 | "\n", 547 | "for x in range(nbr_sequences):\n", 548 | " evolved_seqs[x][:,1] = scaler.inverse_transform(evolved_seqs[x][:, 1])" 549 | ] 550 | }, 551 | { 552 | "cell_type": "markdown", 553 | "metadata": {}, 554 | "source": [ 555 | "### Plot" 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": 14, 561 | "metadata": {}, 562 | "outputs": [ 563 | { 564 | "data": { 565 | "image/png": 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d6fv27eM///kPL7zwAgAul8vHp9o2h9GTeNQxF3uE3G7BuYIqUtL0DSYdc+1Z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\nrkJggg==\n", 566 | "text/plain": [ 567 | "" 568 | ] 569 | }, 570 | "metadata": {}, 571 | "output_type": "display_data" 572 | } 573 | ], 574 | "source": [ 575 | "for x in range(nbr_sequences)[:5]:\n", 576 | " plt.plot(range(evolved_seqs[0].shape[0]), evolved_seqs[x][:,1])\n", 577 | "plt.xlabel('Generations')\n", 578 | "plt.ylabel('Predicted mean ribosome load')\n", 579 | "plt.gcf().set_size_inches((4,4))" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": 15, 585 | "metadata": {}, 586 | "outputs": [ 587 | { 588 | "name": "stdout", 589 | "output_type": "stream", 590 | "text": [ 591 | "119\n" 592 | ] 593 | } 594 | ], 595 | "source": [ 596 | "rb_seqs = {}\n", 597 | "count = 0\n", 598 | "for x in range(len(evolved_seqs)):\n", 599 | " tmp = pd.DataFrame(evolved_seqs[x], columns=['seq', 'pred_rl'])\n", 600 | " tmp['generation'] = tmp.index\n", 601 | " tmp = tmp.drop_duplicates(subset=['seq'])\n", 602 | " tmp.reset_index(inplace=True, drop=True)\n", 603 | " rb_seqs[x] = tmp\n", 604 | " count += len(tmp)\n", 605 | "print count" 606 | ] 607 | }, 608 | { 609 | "cell_type": "code", 610 | "execution_count": null, 611 | "metadata": {}, 612 | "outputs": [], 613 | "source": [] 614 | } 615 | ], 616 | "metadata": { 617 | "kernelspec": { 618 | "display_name": "Python 2", 619 | "language": "python", 620 | "name": "python2" 621 | }, 622 | "language_info": { 623 | "codemirror_mode": { 624 | "name": "ipython", 625 | "version": 2 626 | }, 627 | "file_extension": ".py", 628 | "mimetype": "text/x-python", 629 | "name": "python", 630 | "nbconvert_exporter": "python", 631 | "pygments_lexer": "ipython2", 632 | "version": "2.7.12" 633 | } 634 | }, 635 | "nbformat": 4, 636 | "nbformat_minor": 2 637 | } 638 | --------------------------------------------------------------------------------