genet 0.13.3

GenET: Genome Editing Toolkit

Genome Editing Toolkit
Since 2022. 08. 19.

Welcome to GenET

GenET (Genome Editing Toolkit) is a library of various python functions for the purpose of analyzing and evaluating data from genome editing experiments. 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.

Please see the documentation.

Installation

1/ Create virtual environment and install genet

# Create virtual env for genet. (python 3.8 was tested)
conda create -n genet python=3.8
conda activate genet

# Install genet ( >= ver. 0.7.0)
pip install genet

2/ Install Pytorch (v1.11.0 was tested)

Pytorch ver.2 is not compatible yet.

# For OSX (MacOS)
pip install torch==1.11.0

# 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

# CPU only
pip install torch==1.11.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu

3/ Install ViennaRNA

# install ViennaRNA package for prediction module
conda install viennarna

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.:

• 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.

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). DeepSpCas9 prediction score is calculated simultaneously and requires tensorflow (version >=2.6). DeepPrime was developed on pytorch.

from genet.predict import DeepPrime

seq_wt   = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
seq_ed   = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'

pegrna = DeepPrime('Test', seq_wt, seq_ed, edit_type='sub', edit_len=1)

# check designed pegRNAs
>>> pegrna.features

	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

Next, select model PE system and run DeepPrime

pe2max_output = pegrna.predict(pe_system='PE2max', cell_type='HEK293T')

>>> pe2max_output.head()

	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

The previous function, pe_score(), is still available for use. However, please note that this function will be deprecated in the near future.

from genet import predict as prd

# 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)

seq_wt   = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGAAGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
seq_ed   = 'ATGACAATAAAAGACAACACCCTTGCCTTGTGGAGTTTTCAAAGCTCCCAGAAACTGAGACGAACTATAACCTGCAAATGTCAACTGAAACCTTAAAGTGAGTATTTAATTGAGCTGAAGT'
alt_type = 'sub1'

df_pe = prd.pe_score(seq_wt, seq_ed, alt_type)
df_pe.head()

	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

It is also possible to predict other cell lines (A549, DLD1...) and PE systems (PE2max, PE4max...).

df_pe = prd.pe_score(seq_wt, seq_ed, alt_type, sID='MyGene', pe_system='PE4max', cell_type='A549')

Please send all comments and questions to [e-mail]