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()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

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()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

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)

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}