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Merge pull request #53 from Goosang-Yu/dev_goosang
Update daocs and fix bugs in SynonymousPE.stack
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| <!-- --- | ||
| template: home.html | ||
| title: Home | ||
| social: | ||
| cards_layout_options: | ||
| title: Documentation that simply works | ||
| --- --> | ||
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| # Welcome to GenET | ||
| [](https://badge.fury.io/py/genet) | ||
| [](https://badge.fury.io/py/genet) | ||
| [](https://genethq.slack.com/archives/C04DP727E4E) | ||
| [](https://img.shields.io/pypi/l/ansicolortags.svg) | ||
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| GenET (Genome Editing Toolkit) is a library of various python functions for the purpose of analyzing and evaluating data from genome editing experiments. | ||
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| GenET is still in its early stages of development and continue to improve and expand. Currently planned functions include guideRNA design, saturation library design, deep sequenced data analysis, and guide RNA activity prediction. | ||
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| ## Who should use GenET? | ||
| GenET was developed for anyone interested in the field of genome editing. Especially, Genet can provide aid to those with the following objectives.: <br /> | ||
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| - Develop a quick and easy to design an genome editing experiment for a specific gene. | ||
| - Perform genome editing analysis based on sequening data | ||
| - Predict the activtiy of specific guideRNAs or all guideRNAs designed for editing a specific product. | ||
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| ## Get Help | ||
| The fastest way to get help is through Slack channel. You can also see our Issue log for answers to questions asked in the past by other members or raise a new question if it's not asked before. | ||
| <!-- (만약 FAQ 페이지를 만들면) Check our Frequently Asked Questions (FAQs) page. --> | ||
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| ## Support GenET | ||
| ### ⭐ Star GenET on GitHub | ||
| Give GenET a star on our [GitHub repository](https://github.com/Goosang-Yu/genet) (click the star button on the top right corner) | ||
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| ### 📢 Tweet about GenET | ||
| Help GenET spread the word. We love to hear success stories and use cases. |
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| title: Home | ||
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| cards_layout_options: | ||
| title: Documentation that simply works | ||
| --- --> | ||
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| Welcome to GenET test page. |
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| GenET은 python을 기반으로 다양한 genome editing 관련된 연구 설계와 분석을 할 수 있는 library이다. 특히 CRISPR systme (Cas9, base editing, prime editing)을 손쉽게 활용할 수 있는 기능들을 제공하고 있다. | ||
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| ## Installation | ||
| #### 1/ Create virtual environment and install genet | ||
| ```python | ||
| # Create virtual env for genet. (python 3.8 was tested) | ||
| conda create -n genet python=3.8 | ||
| conda activate genet | ||
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| # Install genet ( >= ver. 0.7.0) | ||
| ## Installation | ||
| GenET은 PyPI를 이용해서 손쉽게 설치할 수 있다. [설치방법 페이지](/genet/installation)를 확인하길 바란다. | ||
| ```bash | ||
| pip install genet | ||
| ``` | ||
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| #### 2/ Install Pytorch (v1.11.0 was tested) | ||
| Pytorch ver.2 is not compatible yet. | ||
| ```python | ||
| # For OSX (MacOS) | ||
| pip install torch==1.11.0 | ||
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| # For Linux and Windows | ||
| # CUDA 11.3 (choose version degending on your GPU) | ||
| pip install torch==1.11.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 | ||
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| # CPU only | ||
| pip install torch==1.11.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu | ||
| ``` | ||
| #### 3/ Install ViennaRNA | ||
| ```python | ||
| # install ViennaRNA package for prediction module | ||
| conda install viennarna | ||
| ``` | ||
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| ## Who should use GenET? | ||
| GenET was developed for anyone interested in the field of genome editing. Especially, Genet can provide aid to those with the following objectives.: <br /> | ||
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| - Develop a quick and easy to design an genome editing experiment for a specific gene. | ||
| - Perform genome editing analysis based on sequening data | ||
| - Predict the activtiy of specific guideRNAs or all guideRNAs designed for editing a specific product. | ||
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| ## Example: Prediction of prime editing efficiency by DeepPrime | ||
|  | ||
| DeepPrime is a prediction model for evaluating prime editing guideRNAs (pegRNAs) that target specific target sites for prime editing ([Yu et al. Cell 2023](https://doi.org/10.1016/j.cell.2023.03.034)). DeepSpCas9 prediction score is calculated simultaneously and requires tensorflow (version >=2.6). DeepPrime was developed on pytorch. | ||
| ## Genome editing 연구를 위한 GenET 활용 예시 | ||
| GenET을 통해, 유전 정보 및 CRISPR를 이용한 연구를 하기 위해 필요한 다양한 기능들을 사용할 수 있다 (또는 추가 예정). | ||
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| ```python | ||
| from genet.predict import DeepPrime | ||
| > Case1: | ||
| seq_wt = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT' | ||
| seq_ed = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT' | ||
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| pegrna = DeepPrime('Test', seq_wt, seq_ed, edit_type='sub', edit_len=1) | ||
| ```python | ||
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| # check designed pegRNAs | ||
| >>> pegrna.features | ||
| ``` | ||
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| | | ID | WT74_On | Edited74_On | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | type_sub | type_ins | type_del | Tm1 | Tm2 | Tm2new | Tm3 | Tm4 | TmD | nGCcnt1 | nGCcnt2 | nGCcnt3 | fGCcont1 | fGCcont2 | fGCcont3 | MFE3 | MFE4 | DeepSpCas9_score | | ||
| | - | ---- | -------------------------------------------------------------------------- | -------------------------------------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | -------- | -------- | -------- | -------- | ------- | ------- | --------- | -------- | --------- | ------- | ------- | ------- | -------- | -------- | -------- | ------ | ----- | ---------------- | | ||
| | 0 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxxxCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 7 | 35 | 42 | 34 | 1 | 1 | 1 | 0 | 0 | 16.19097 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 5 | 16 | 21 | 71.42857 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 | | ||
| | 1 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxxCCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 8 | 35 | 43 | 34 | 1 | 1 | 1 | 0 | 0 | 30.19954 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 6 | 16 | 22 | 75 | 45.71429 | 51.16279 | \-10.4 | \-0.6 | 45.96754 | | ||
| | 2 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxxACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 9 | 35 | 44 | 34 | 1 | 1 | 1 | 0 | 0 | 33.78395 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 6 | 16 | 22 | 66.66667 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 | | ||
| | 3 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxxCACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 10 | 35 | 45 | 34 | 1 | 1 | 1 | 0 | 0 | 38.51415 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 70 | 45.71429 | 51.11111 | \-10.4 | \-0.6 | 45.96754 | | ||
| | 4 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxxACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 11 | 35 | 46 | 34 | 1 | 1 | 1 | 0 | 0 | 40.87411 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 63.63636 | 45.71429 | 50 | \-10.4 | \-0.6 | 45.96754 | | ||
| | 5 | Test | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATG | xxxxxxxxxAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGxxxxxxxxxxxxxxxxxx | 12 | 35 | 47 | 34 | 1 | 1 | 1 | 0 | 0 | 40.07098 | 62.1654 | 62.1654 | \-277.939 | 58.22525 | \-340.105 | 7 | 16 | 23 | 58.33333 | 45.71429 | 48.93617 | \-10.4 | \-0.6 | 45.96754 | | ||
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| Next, select model PE system and run DeepPrime | ||
| ```python | ||
| pe2max_output = pegrna.predict(pe_system='PE2max', cell_type='HEK293T') | ||
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| >>> pe2max_output.head() | ||
| ``` | ||
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| | | Target | Spacer | RT-PBS | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | PE2max_score | | ||
| | - | ------------------------------------------------- | ------------------------------ | ---------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | ------------ | | ||
| | 0 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | 0.904907 | | ||
| | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | 2.377118 | | ||
| | 2 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | 2.613841 | | ||
| | 3 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | 3.643573 | | ||
| | 4 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | 3.770234 | | ||
| ## GenET에서 제공하는 기능들 | ||
| GenET에서 제공 (예정 포함)하는 기능들을 아래와 같다. | ||
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| | Module | Functions | Descriptions | Status | | ||
| | -------- | -------------- | --------------------------------------------------------------------- | ------ | | ||
| | Predict | SpCas9 | DeepSpCas9 모델 사용 | 사용가능 | | ||
| | Predict | SpCas9variants | DeepSpCas9variants 모델 사용 | 사용가능 | | ||
| | Predict | Base editor | DeepBE 모델 사용 | 개발예정 | | ||
| | Predict | Prime editor | DeepPrime 모델 사용 | 사용가능 | | ||
| | Design | KOLiD | Genome-wide KO library design | 개발예정 | | ||
| | Design | ReLiD | Gene regulation library design | 개발예정 | | ||
| | Design | CRISPRStop | Design gRNA for inducing premature stop codon using CBE | 개발예정 | | ||
| | Design | SynonymousPE | Design pegRNA containing additional synonymousmutation in RT template | 사용가능 | | ||
| | Database | GetGenome | NCBI database에서 genome data를 가져오는 기능 | 사용가능 | | ||
| | Database | GetGene | NCBI database에서 특정 gene의 정보를 가져오는 기능 | 개발예정 | | ||
| | Database | GenBankParser | GenBank file에서 원하는 정보들을 찾아내는 기능 | 개발예정 | | ||
| | Database | DFConverter | NCBI genbank file의 형태를 DataFrame으로 변환하는 기능 | 사용가능 | | ||
| | Analysis | SGE | Saturation genome editing 데이터를 분석하기 위한 기능 | 개발예정 | | ||
| | Analysis | UMItools | UMI 분석을 위한 함수 (from UMI-tools) | 사용가능 | | ||
| | Utils | request_file | HTTP protocol을 이용해 서버에서 원하는 파일을 다운로드 하는 | 사용가능 | | ||
| | Utils | SplitFastq | FASTQ 파일을 작은 크기들로 나눠주는 기능 | 사용가능 | | ||
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| The previous function, ```pe_score()```, is still available for use. However, please note that this function will be deprecated in the near future. | ||
| ```python | ||
| from genet import predict as prd | ||
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| # Place WT sequence and Edited sequence information, respectively. | ||
| # And select the edit type you want to make and put it in. | ||
| #Input seq: 60bp 5' context + 1bp center + 60bp 3' context (total 121bp) | ||
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| seq_wt = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT' | ||
| seq_ed = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT' | ||
| alt_type = 'sub1' | ||
| ## Need help? | ||
| Look at the issues section to find out about specific cases and others. | ||
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| df_pe = prd.pe_score(seq_wt, seq_ed, alt_type) | ||
| df_pe.head() | ||
| ``` | ||
| If you still have doubts or cannot solve the problem, please consider opening an issue | ||
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| | | Target | Spacer | RT-PBS | PBSlen | RTlen | RT-PBSlen | Edit_pos | Edit_len | RHA_len | PE2max_score | | ||
| | - | ------------------------------------------------- | ------------------------------ | ---------------------------------------------- | ------ | ----- | --------- | -------- | -------- | ------- | ------------ | | ||
| | 0 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGG | 7 | 35 | 42 | 34 | 1 | 1 | 0.904907 | | ||
| | 1 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGG | 8 | 35 | 43 | 34 | 1 | 1 | 2.377118 | | ||
| | 2 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGT | 9 | 35 | 44 | 34 | 1 | 1 | 2.613841 | | ||
| | 3 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTG | 10 | 35 | 45 | 34 | 1 | 1 | 3.643573 | | ||
| | 4 | ATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGA... | ATAAAAGACAACACCCTTGCCTTGTGGAGT | CGTCTCAGTTTCTGGGAGCTTTGAAAACTCCACAAGGCAAGGGTGT | 11 | 35 | 46 | 34 | 1 | 1 | 3.770234 | | ||
| Please send all comments and questions to [email protected] | ||
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| It is also possible to predict other cell lines (A549, DLD1...) and PE systems (PE2max, PE4max...). | ||
| ## GenET 인용하기 | ||
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| ```python | ||
| df_pe = prd.pe_score(seq_wt, seq_ed, alt_type, sID='MyGene', pe_system='PE4max', cell_type='A549') | ||
| ``` | ||
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| Please send all comments and questions to [email protected] | ||
| @Manual {GenET, | ||
| title = {GenET: Python package for genome editing research}, | ||
| author = {Goosang Yu}, | ||
| year = {2024}, | ||
| month = {January}, | ||
| note = {GenET version 0.13.1}, | ||
| url = {https://github.com/Goosang-Yu/genet} | ||
| } | ||
| ``` |
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