This package takes concensus fasta files from the MrHamer2.0 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.
pip install Chimera_Bustergit clone https://github.com/JessicaA2019/Chimera_Buster.git
cd Chimera_Buster
python setup.py installSome dependencies currently do not install with Chimera_Buster. Please pip install the following dependencies:
- edlib
- pandas
- multiprocessing
- argparse
Chimera_Buster [options] {input_file_name} {input_prelim_name} {output_file_prefix}
To run the pipeline the following input files are required:
| Input | Description |
|---|---|
| input_file_name | Designates clusters_concesus.fasta file from the clustering_consensus folder to be filtered. This is required. |
| input_prelim_name | Designates clusters_concesus.fasta file from the clustering 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. |
| -c True/False, --check_clusters True/False | When set to true, chimeric reads are rechecked to account for any clustering issues earlier in the pipeline. WARNING: this part of the code is slow and is only recommended for low input samples. Default is False. |
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}_non_chimeras.csv | A csv of the sample ID, UMI sequences, and cluster counts for each nonchimeric read. |
For issues or bugs, please report them on the issues page.
MIT - Copyright (c) 2024 Jessica Lauren Albert
