Chimera-Buster 1.1.3

eliminates chimeric reads by comparing the UMI sequences and finding any matches in the 5 prime or 3 prime UMIs and keeping the sequence that has the highest prevalence

Chimera_Buster

Overview

This package takes concensus fasta files from the MrHamer pipeline and eliminates chimeric reads by comparing the UMI sequences and finding any matches in the 5' or 3' UMIs and keeping the sequence that has the highest prevalence.

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Installation

Requires python<=3.10

Quick Install

pip install Chimera_Buster

Manual Install

git clone https://github.com/JessicaA2019/Chimera_Buster.git 
cd Chimera_Buster
python setup.py install

Dependencies

Some dependencies currently do not install with Chimera_Buster. Please pip install the following dependencies:

• edlib
• pandas
• multiprocessing
• argparse

Testing

To test if the installation was successful run the following command with the test files provided:

Chimera_Buster CB_test_files CB_test

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Usage

Chimera_Buster [options] {input_folder_name} {output_file_prefix}

Inputs

To run the pipeline the following input files are required:

Input	Description
input_folder_name	Designates MrHAMER folder to be filtered. This is required.
output_file_prefix	Designates output file prefix. This is required.
The following input files are optional:
Arguement	Function
------	------
-h, --help	Prints help message to terminal.
-m int, --mismatch int	Designates the maximum number of mismatched/indel bases allowed when comparing UMIs. Default is 1.

Outputs

The main output files created by the pipeline are:

Output	Description
{output_file_prefix}_chimera_list.txt	A list of all chimeric sample IDs.
{output_file_prefix}_nonchimera_list.txt	A list of all nonchimeric sample IDs.
{output_file_prefix}_chimeras.csv	A csv of the sample ID, UMI sequences, and cluster counts for each chimeric read.
{output_file_prefix}_nonchimeras.csv	A csv of the sample ID, UMI sequences, and cluster counts for each nonchimeric read.

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Help

For issues or bugs, please report them on the issues page.

License

MIT - Copyright (c) 2024 Jessica Lauren Albert