poola-be 0.0.1

Python package to analyze the results of base editor screens

poola_be

Python package for base editor screens

Install

pip install poola_be

How to use

To demonstrate the use of these functions, we will first design a base editor tiling library with guides tiling the transcript ENST00000380152 of BRCA2. These guides are annotated with predicted edits using the C>T base editor in the window of nucleotide 4-8.

from poola_be import core as pool_be
import pandas as pd

design_df = pd.read_csv('sample_input/crisprbe-guides.txt', sep='\t')
design_df.head()

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	Input	CRISPR Enzyme	Edit Type	Edit Window	Target Assembly	Target Genome Sequence	Target Gene ID	Target Gene Symbol	Target Gene Strand	Target Transcript ID	...	PAM Sequence	sgRNA Target Sequence Start Pos. (global)	sgRNA Orientation	Nucleotide Edits (global)	Guide Edits	Nucleotide Edits	Amino Acid Edits	Mutation Category	Constraint Violations	Note
0	ENST00000380152	SpyoCas9	C-T	4..8	GRCh38 (9606)	NC_000013.11	ENSG00000139618	BRCA2	+	ENST00000380152.8	...	TGG	32316449	sense	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	ENST00000380152	SpyoCas9	C-T	4..8	GRCh38 (9606)	NC_000013.11	ENSG00000139618	BRCA2	+	ENST00000380152.8	...	AGG	32316462	sense	32316465C>T	C_4	5C>T	Pro2Leu	Missense	NaN	NaN
2	ENST00000380152	SpyoCas9	C-T	4..8	GRCh38 (9606)	NC_000013.11	ENSG00000139618	BRCA2	+	ENST00000380152.8	...	AGG	32316467	antisense	32316479G>A;32316481G>A, 32316483G>A	C_8_6, C_4	19G>A;21G>A, 23G>A	Glu7Lys, Arg8Lys	Missense, Missense	NaN	NaN
3	ENST00000380152	SpyoCas9	C-T	4..8	GRCh38 (9606)	NC_000013.11	ENSG00000139618	BRCA2	+	ENST00000380152.8	...	TGG	32316477	antisense	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	ENST00000380152	SpyoCas9	C-T	4..8	GRCh38 (9606)	NC_000013.11	ENSG00000139618	BRCA2	+	ENST00000380152.8	...	TGG	32316488	antisense	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 23 columns

Assign severe mutation bin

As noted in the "Mutation Category" column, each guide is predicted to make more one or more types of mutations if Cs are present in the editing window. We can then annotate each guide with the most severe mutation bin in the order Nonsense > Splice site > Missense > Intron > Silent > UTR > no edit.

design_df['Mutation Bin'] = design_df['Mutation Category'].apply(pool_be.get_most_severe_mutation_type)
design_df[['sgRNA Target Sequence','Mutation Category','Mutation Bin']].head()

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	sgRNA Target Sequence	Mutation Category	Mutation Bin
0	TCGTAGGTAAAAATGCCTAT	NaN	No edits
1	TGCCTATTGGATCCAAAGAG	Missense	Missense
2	GGCCTCTCTTTGGATCCAAT	Missense, Missense	Missense
3	AAAAAATGTTGGCCTCTCTT	NaN	No edits
4	TTAAAAATTTCAAAAAATGT	NaN	No edits

Calculate median residue

We can then get the median residue of the predicted edits.

design_df['Median Residue'] = design_df.apply(lambda x: pool_be.get_median_residues(x['Mutation Bin'], x['Amino Acid Edits']), axis=1)
design_df[['sgRNA Target Sequence','Amino Acid Edits','Mutation Category','Mutation Bin','Median Residue']].head(15)

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	sgRNA Target Sequence	Amino Acid Edits	Mutation Category	Mutation Bin	Median Residue
0	TCGTAGGTAAAAATGCCTAT	NaN	NaN	No edits	NaN
1	TGCCTATTGGATCCAAAGAG	Pro2Leu	Missense	Missense	2.0
2	GGCCTCTCTTTGGATCCAAT	Glu7Lys, Arg8Lys	Missense, Missense	Missense	7.5
3	AAAAAATGTTGGCCTCTCTT	NaN	NaN	No edits	NaN
4	TTAAAAATTTCAAAAAATGT	NaN	NaN	No edits	NaN
5	AAGACACGCTGCAACAAAGC	Thr17Ile, Arg18Cys	Missense, Missense	Missense	17.5
6	TTTTTTTTTTAAATAGATTT	NaN	NaN	No edits	NaN
7	TAGGACCAATAAGTCTTAAT	Pro26Leu	Missense	Missense	26.0
8	TCAAACCAATTAAGACTTAT	Trp31Ter	Nonsense	Nonsense	31.0
9	GCAGGTTCAGAATTATAGGG	Glu45Lys	Missense	Missense	45.0
10	TCTGCAGGTTCAGAATTATA	Ala47Thr	Missense	Missense	47.0
11	TTCTGCAGGTTCAGAATTAT	Ala47Thr	Missense	Missense	47.0
12	TTATGTTCAGATTCTTCTGC	Glu51Lys	Missense	Missense	51.0
13	TGTGGAGTTTTAAATAGGTT	NaN	NaN	No edits	NaN
14	ACCTATTTAAAACTCCACAA	NaN	NaN	No edits	NaN