rs3 0.0.5

Predict the activity of CRISPR sgRNAs

Rule Set 3

Python package to predict the activity of CRISPR sgRNA sequences using Rule Set 3

Install

pip install git+ssh://[e-mail]/gpp-rnd/rs3.git

Quick Start

Sequence based model

Import packages

from rs3.seq import predict_seq

Create a list of context sequences you want to predict

context_seqs = ['GACGAAAGCGACAACGCGTTCATCCGGGCA', 'AGAAAACACTAGCATCCCCACCCGCGGACT']

You can predict on-target scores for sequences using the predict_seq function, specifying either Hsu2013 or Chen2013 as the tracrRNA to score with

predict_seq(context_seqs, sequence_tracr='Hsu2013')

array([-0.86673522,  1.09560723])

Target and sequence scores

Using the predict function we can calculate both target scores and sequence scores. Target-based scores use information such as amino acid sequence and whether the sgRNA targets in a protein domain.

import pandas as pd
from rs3.predict import predict
import gpplot
import seaborn as sns
import matplotlib.pyplot as plt

We'll use a list of ~250 sgRNA from the GeckoV2 library as an example dataset

design_df = pd.read_table('test_data/sgrna-designs.txt')
design_df

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	Input	Quota	Target Taxon	Target Gene ID	Target Gene Symbol	Target Transcript	Target Reference Coords	Target Alias	CRISPR Mechanism	Target Domain	...	On-Target Rank Weight	Off-Target Rank Weight	Combined Rank	Preselected As	Matching Active Arrayed Oligos	Matching Arrayed Constructs	Pools Containing Matching Construct	Pick Order	Picking Round	Picking Notes
0	PSMB7	2	9606	ENSG00000136930	PSMB7	ENST00000259457.8	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	7	GCAGATACAAGAGCAACTGA	NaN	BRDN0004619103	NaN	1	0	Preselected
1	PSMB7	2	9606	ENSG00000136930	PSMB7	ENST00000259457.8	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	48	AAAACTGGCACGACCATCGC	NaN	NaN	NaN	2	0	Preselected
2	PRC1	2	9606	ENSG00000198901	PRC1	ENST00000394249.8	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	7	AAAAGATTTGCGCACCCAAG	NaN	NaN	NaN	1	0	Preselected
3	PRC1	2	9606	ENSG00000198901	PRC1	ENST00000394249.8	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	8	CTTTGACCCAGACATAATGG	NaN	NaN	NaN	2	0	Preselected
4	TOP1	2	9606	ENSG00000198900	TOP1	ENST00000361337.3	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	1	NaN	NaN	BRDN0001486452	NaN	2	1	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
395	RFC5	2	9606	ENSG00000111445	RFC5	ENST00000454402.7	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	23	TTTATATAGCTGTTTCGCAC	NaN	NaN	NaN	1	0	Preselected
396	NXT1	2	9606	ENSG00000132661	NXT1	ENST00000254998.3	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	3	NaN	NaN	BRDN0002419367	NaN	2	1	NaN
397	NXT1	2	9606	ENSG00000132661	NXT1	ENST00000254998.3	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	31	TTTGCTGTCCCGCCTGTACA	NaN	NaN	NaN	1	0	Preselected
398	NOL10	2	9606	ENSG00000115761	NOL10	ENST00000381685.10	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	3	NaN	NaN	NaN	NaN	2	1	NaN
399	NOL10	2	9606	ENSG00000115761	NOL10	ENST00000381685.10	NaN	NaN	CRISPRko	CDS	...	1.0	1.0	14	TTTGTCTGATGACTACTCAA	NaN	NaN	NaN	1	0	Preselected

400 rows × 60 columns

gecko_activity = pd.read_csv('test_data/Aguirre2017_activity.csv')
gecko_activity

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	sgRNA Sequence	sgRNA Context Sequence	Target Gene Symbol	Target Cut %	avg_mean_centered_neg_lfc
0	AAAAAACTTACCCCTTTGAC	AAAAAAAAAACTTACCCCTTTGACTGGCCA	CPSF6	22.2	-1.139819
1	AAAAACATTATCATTGAGCC	TGGCAAAAACATTATCATTGAGCCTGGATT	SKA3	62.3	-0.793055
2	AAAAAGAGATTGTCAAATCA	TATGAAAAAGAGATTGTCAAATCAAGGTAG	AQR	3.8	0.946453
3	AAAAAGCATCTCTAGAAATA	TTCAAAAAAGCATCTCTAGAAATATGGTCC	ZNHIT6	61.7	-0.429590
4	AAAAAGCGAGATACCCGAAA	AAAAAAAAAGCGAGATACCCGAAAAGGCAG	ABCF1	9.4	0.734196
...	...	...	...	...	...
8654	TTTGTGGCAGCGAATCATAA	TGTCTTTGTGGCAGCGAATCATAATGGTTC	UMPS	43.8	-0.927345
8655	TTTGTTAATATCTGCTGAAC	TGAATTTGTTAATATCTGCTGAACAGGAGT	GTF2A1	40.3	-0.382060
8656	TTTGTTAGGATGTGCATTCC	TTTCTTTGTTAGGATGTGCATTCCAGGTAC	NAT10	16.4	-0.927645
8657	TTTGTTAGGTCATCGTATTG	GGTTTTTGTTAGGTCATCGTATTGAGGAAG	RPL4	33.5	-1.425502
8658	TTTGTTCCTTAGTTGCTGAC	TACTTTTGTTCCTTAGTTGCTGACAGGTCC	MRPL47	34.8	-1.268444

8659 rows × 5 columns

By listing both tracrRNA tracr=['Hsu2013', 'Chen2013'] and setting target=True, we calculate 5 unique scores: one sequence score for each tracr, the target score, and the sequence scores plus the target score.

scored_designs = predict(design_df, tracr=['Hsu2013', 'Chen2013'], target=True,
                         n_jobs=2)
scored_designs

Getting amino acid sequences


100%|██████████| 4/4 [00:00<00:00, 94.47it/s]


Getting protein domains


100%|██████████| 200/200 [00:30<00:00,  6.50it/s]
/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator SimpleImputer from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
  warnings.warn(
/Users/pdeweird/opt/anaconda3/envs/rs3/lib/python3.8/site-packages/sklearn/base.py:310: UserWarning: Trying to unpickle estimator Pipeline from version 1.0.dev0 when using version 0.24.2. This might lead to breaking code or invalid results. Use at your own risk.
  warnings.warn(

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	Input	Quota	Target Taxon	Target Gene ID	Target Gene Symbol	Target Transcript	Target Reference Coords	Target Alias	CRISPR Mechanism	Target Domain	...	Pools Containing Matching Construct	Pick Order	Picking Round	Picking Notes	RS3 Sequence Score (Hsu2013 tracr)	RS3 Sequence Score (Chen2013 tracr)	Transcript Base	RS3 Target Score	RS3 Sequence (Hsu2013 tracr) + Target Score	RS3 Sequence (Chen2013 tracr) + Target Score
0	PSMB7	2	9606	ENSG00000136930	PSMB7	ENST00000259457.8	NaN	NaN	CRISPRko	CDS	...	NaN	1	0	Preselected	0.750904	0.512534	ENST00000259457	0.273974	1.024878	0.786508
1	PSMB7	2	9606	ENSG00000136930	PSMB7	ENST00000259457.8	NaN	NaN	CRISPRko	CDS	...	NaN	2	0	Preselected	-0.218514	-0.095684	ENST00000259457	-0.010152	-0.228667	-0.105837
2	PRC1	2	9606	ENSG00000198901	PRC1	ENST00000394249.8	NaN	NaN	CRISPRko	CDS	...	NaN	1	0	Preselected	-0.126708	-0.307830	ENST00000394249	-0.018259	-0.144967	-0.326089
3	PRC1	2	9606	ENSG00000198901	PRC1	ENST00000394249.8	NaN	NaN	CRISPRko	CDS	...	NaN	2	0	Preselected	0.690050	0.390095	ENST00000394249	-0.089659	0.600392	0.300436
4	TOP1	2	9606	ENSG00000198900	TOP1	ENST00000361337.3	NaN	NaN	CRISPRko	CDS	...	NaN	2	1	NaN	0.451508	-0.169016	ENST00000361337	-0.018748	0.432760	-0.187764
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
395	RFC5	2	9606	ENSG00000111445	RFC5	ENST00000454402.7	NaN	NaN	CRISPRko	CDS	...	NaN	1	0	Preselected	-0.220600	-0.022154	ENST00000454402	0.102902	-0.117698	0.080747
396	NXT1	2	9606	ENSG00000132661	NXT1	ENST00000254998.3	NaN	NaN	CRISPRko	CDS	...	NaN	2	1	NaN	0.621609	0.539656	ENST00000254998	0.220856	0.842465	0.760513
397	NXT1	2	9606	ENSG00000132661	NXT1	ENST00000254998.3	NaN	NaN	CRISPRko	CDS	...	NaN	1	0	Preselected	0.119830	0.012744	ENST00000254998	0.146767	0.266597	0.159511
398	NOL10	2	9606	ENSG00000115761	NOL10	ENST00000381685.10	NaN	NaN	CRISPRko	CDS	...	NaN	2	1	NaN	0.798633	0.646323	ENST00000381685	-0.039771	0.758861	0.606552
399	NOL10	2	9606	ENSG00000115761	NOL10	ENST00000381685.10	NaN	NaN	CRISPRko	CDS	...	NaN	1	0	Preselected	0.283254	0.264148	ENST00000381685	0.015462	0.298716	0.279611

400 rows × 66 columns

gecko_activity_scores = (gecko_activity.merge(scored_designs,
                                              how='inner',
                                              on=['sgRNA Sequence', 'sgRNA Context Sequence',
                                                  'Target Gene Symbol', 'Target Cut %']))
gecko_activity_scores

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	sgRNA Sequence	sgRNA Context Sequence	Target Gene Symbol	Target Cut %	avg_mean_centered_neg_lfc	Input	Quota	Target Taxon	Target Gene ID	Target Transcript	...	Pools Containing Matching Construct	Pick Order	Picking Round	Picking Notes	RS3 Sequence Score (Hsu2013 tracr)	RS3 Sequence Score (Chen2013 tracr)	Transcript Base	RS3 Target Score	RS3 Sequence (Hsu2013 tracr) + Target Score	RS3 Sequence (Chen2013 tracr) + Target Score
0	AAAACTGGCACGACCATCGC	CCGGAAAACTGGCACGACCATCGCTGGGGT	PSMB7	16.4	-1.052943	PSMB7	2	9606	ENSG00000136930	ENST00000259457.8	...	NaN	2	0	Preselected	-0.218514	-0.095684	ENST00000259457	-0.010152	-0.228667	-0.105837
1	AAAAGATTTGCGCACCCAAG	TAGAAAAAGATTTGCGCACCCAAGTGGAAT	PRC1	17.0	0.028674	PRC1	2	9606	ENSG00000198901	ENST00000394249.8	...	NaN	1	0	Preselected	-0.126708	-0.307830	ENST00000394249	-0.018259	-0.144967	-0.326089
2	AAAAGTCCAAGCATAGCAAC	CGGGAAAAGTCCAAGCATAGCAACAGGTAA	TOP1	6.5	0.195309	TOP1	2	9606	ENSG00000198900	ENST00000361337.3	...	NaN	1	0	Preselected	-0.356580	-0.082514	ENST00000361337	-0.418276	-0.774856	-0.500790
3	AAAGAAGCCTCAACTTCGTC	AGCGAAAGAAGCCTCAACTTCGTCTGGAGA	CENPW	37.5	-1.338209	CENPW	2	9606	ENSG00000203760	ENST00000368328.5	...	NaN	2	0	Preselected	-0.663540	-0.303324	ENST00000368328	0.274739	-0.388801	-0.028585
4	AAAGTGTGCTTTGTTGGAGA	TACTAAAGTGTGCTTTGTTGGAGATGGCTT	NSA2	60.0	-0.175219	NSA2	2	9606	ENSG00000164346	ENST00000610426.5	...	NaN	2	0	Preselected	-0.413636	-0.585179	ENST00000610426	-0.072158	-0.485794	-0.657337
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
247	TTTAGCCGGATGCGCAGTGA	CTCTTTTAGCCGGATGCGCAGTGATGGTTT	NKAPD1	20.6	0.627322	NKAPD1	2	9606	ENSG00000150776	ENST00000393047.8	...	CP1718	1	0	Preselected	0.298329	0.274344	ENST00000393047	-0.201158	0.097171	0.073187
248	TTTATATAGCTGTTTCGCAC	TGTCTTTATATAGCTGTTTCGCACAGGCTA	RFC5	21.5	-0.957190	RFC5	2	9606	ENSG00000111445	ENST00000454402.7	...	NaN	1	0	Preselected	-0.220600	-0.022154	ENST00000454402	0.102902	-0.117698	0.080747
249	TTTGCTGTCCCGCCTGTACA	GGCGTTTGCTGTCCCGCCTGTACATGGGCA	NXT1	27.2	0.176827	NXT1	2	9606	ENSG00000132661	ENST00000254998.3	...	NaN	1	0	Preselected	0.119830	0.012744	ENST00000254998	0.146767	0.266597	0.159511
250	TTTGTCTGATGACTACTCAA	AAATTTTGTCTGATGACTACTCAAAGGTAT	NOL10	15.6	-0.043965	NOL10	2	9606	ENSG00000115761	ENST00000381685.10	...	NaN	1	0	Preselected	0.283254	0.264148	ENST00000381685	0.015462	0.298716	0.279611
251	TTTGTTAGGTCATCGTATTG	GGTTTTTGTTAGGTCATCGTATTGAGGAAG	RPL4	33.5	-1.425502	RPL4	2	9606	ENSG00000174444	ENST00000307961.11	...	CP1718	2	1	NaN	-0.636302	-0.575100	ENST00000307961	0.021391	-0.614912	-0.553709

252 rows × 67 columns

Since Gecko was screened with the tracrRNA from Hsu et al. 2013, we'll use this as our predictor

plt.subplots(figsize=(4,4))
gpplot.point_densityplot(gecko_activity_scores, y='avg_mean_centered_neg_lfc',
                         x='RS3 Sequence (Hsu2013 tracr) + Target Score')
gpplot.add_correlation(gecko_activity_scores, y='avg_mean_centered_neg_lfc',
                       x='RS3 Sequence (Hsu2013 tracr) + Target Score')
sns.despine()