poola-be 0.0.1

Description:

poolabe 0.0.1

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

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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