crispr_cas9

crispr_cas9 contains in-house scripts that are used for NGS data analysis by HBV crispr/cas9 research group.

Installation

pip install crispr_cas9

Usage

crispr_cas9 is intended to be used in Jupyter Notebook. Create folder named input_data and put your fastas into it. Navigate into the directory which contains the input_data folder. Then import the package :

import crispr_cas9

and run :

%run -m crispr_cas9.count_indels
%run -m crispr_cas9.create_matrices
%run -m crispr_cas9.create_plots

Description

This script takes fasta alignment as an input. The input file is the result of deep sequencing reads mapping onto the reference sequence and is imported from the Geneious software.

count_indels.py counts exact number of insertions and deletions. The script saves excel spreadsheets containing the raw count.

create_matrices.py creates matrices with each entry as the number of indels of length x (row) at position y (column), and saves the matrices as excel spreadsheets.

create_plots.py uses the output of the create_matrices.py script and constructs bar chart and heatmap

The example of output file. Here are all the deletions found irrespective of their length:

And heatmap which shows both percent of indels and their lengths:

Requirements

• Python 3
• Biopython
• matplotlib
• numpy
• pandas
• seaborn

The script was used to process sequence data for the work by Kostyushev et al.(2019) [1].

reference:

1. Orthologous CRISPR/Cas9 systems for specific and efficient degradation of covalently closed
   circular DNA of hepatitis B virus. Dmitry Kostyushev, Sergey Brezgin, Anastasiya Kostyusheva, Dmitry Zarifyan, Irina
   Goptar, Vladimir Chulanov. Cellular and Molecular Life Sciences (accepted)