rq-count_v1

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In Silico Workflow for Calculating the Richness of Gene Deletion Mutants in Mixed Samples from Raw Paired-End Short-Read Whole Genome Sequencing Data

Introduction

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rq-count_v1 is a comprehensive in silico workflow designed to simplify the processing of raw paired-end short-read whole genome sequencing (WGS) data derived from bacterial samples containing various gene-deletion mutant strains. This workflow includes a well-structured Snakemake script for reads pre-processing and mapping. Additionally, the "scripts" folder houses corresponding Python scripts that allow for the extraction of mapping information and the calculation of mutant richness within the sample.

Key Features

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• Snakemake Script: The provided Snakemake script efficiently handles reads pre-processing and mapping, ensuring a smooth and organized workflow.
• Python Scripts: The "scripts" folder houses Python scripts that enable users to extract mapping information and calculate the richness of gene deletion mutants in their samples.

Dependencies

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1. python 3.11.3 and following librarires are required;

• Biopython 1.79
• pandas 1.4.2
• scikit-learn 1.1.1

2. snakemake 7.25.3;
3. conda 23.5.0;

Workflow overview

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• Overview of rq-count_v1 workflow.

Quick Start (run it on Linux system)

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Installation

• git clone the rq-count_v1 folder to your local computer;

$ git clone https://github.com/china-fix/rq-count_v1.git

• export the rq-count_v1 folder to PATH (optional);
• install conda and create a new snakemake env;

$ conda install -n base -c conda-forge mamba
$ conda activate base
$ conda activate snakemake
$ snakemake --help

• install python biopython pandas scikit-learn

$ conda install python biopython pandas scikit-learn -n snakemake

Usage

Raw reads pre-processing and mapping

• In your working folder, establish a structured directory with the following arrangement:

1. The primary directory is named "in."
2. Inside the "in" directory, create a subfolder named "REF."In this "REF" subfolder, you should place the reference genome in FASTA format. Ensure that the reference file is named with a ".REF" extension. For example, you can name it "reference_genome.fasta.REF."
3. Alongside the "REF" subfolder, place your raw sequencing reads data files. Each sample should consist of two files, typically denoted as "_1.fq.gz" and "_2.fq.gz" to represent the paired-end reads. For instance, if you have two samples, "sample1" and "sample2," you would place their corresponding read files as "your_raw_reads_sample1_1.fq.gz," "your_raw_reads_sample1_2.fq.gz," "your_raw_reads_sample2_1.fq.gz," and "your_raw_reads_sample2_2.fq.gz." This organized folder structure will facilitate efficient data management and analysis for your sequencing project.

└── in
    ├── REF
    │   └── your_reference_fasta_file.REF
    ├── your_raw_reads_sample1_1.fq.gz
    ├── your_raw_reads_sample1_2.fq.gz
    ├── your_raw_reads_sample2_1.fq.gz
    └── your_raw_reads_sample2_2.fq.gz

• run the command in your working direcotry

snakemake -s Snakefile -c 8 --use-conda

• New folder named report will be created in your working directory, the .tab file recorded the mapping depth which is comare with our python script relative_mapping_caculation_V2.0.py

Calculate the relative richness of deletion-mutant

• prepare a fasta file recording all your gene-deletion sequence, the format looks like below

>KO_gene1
tacagcaacggactgaagaagtaaaacagtcgctcggcgacacgttgccataatggacgttttagccataaacgggcatcgagcagacgtgaacgcgaaatataatcgtcctgaacggcggcgaggtcagcaccaaaacctttatcgtcgattgccagggtaatctcgaaatttagccacagactacgcatatcaaggttaactgtgccaaccagacttagttcgccatcgaccagcacgctcttggtatgcagtaacccgccttcaaactgataaattttaaccccagcagccagcagttccgtaaagaatgcgcgactggcccagccgaccagcatcgagtcattttttcgcggaaggataatactgacatccaccccgcgctgcgccgccgtgcaaatcgcatgaagtaaatcatcgcttggcacaaagtagggcgtggtcatgatcaaatattcacgcgccgaataagccgcagtcaataatgcctggtgaatgagatcttccggaaagccggggccagaagcaattgtgtgaatggtgtgaccgctggcctgttcaaacggcataatattgacatctggtggtggcggcagaatacgttttccggtttcaatctcccagtcgcaggaataaataatccccatcgcggtggcgatagggccttccatacgcgccatcagatcaatccattgccctacgcccgcatcttgtttgaagtagcgaggatcgaccatattcatgctgccggtgtacgcgatgtaattatcgatcatgatcatcttgcgatgttggcgcaggtccatacggcgtaaaaacacacgcatcagattgacctttaaggcttcgaccacttcaataccggcattacgcattagctcgggccacgggctgcggaaaaaagccacactcccggcggagtcgagcatcaatcggcaatgaatgccgcgtcgcgcagccgccattaatgattcagccacctggtccgccatgccgccgggctgccagatataaaacaccatctcaatattatggcgcgcgagctggatgtcgcggattaacgcctgcatcacatcatctgactcggtcatcagttgtagctgattccctttgaccccagcgatcccctgacgacgctcgcaaagcttgaataatggcgcagcgacactgctattttcttcggcgaagatatgcttacaggctttaaggtcgttaagccattttgcggtggaaggccacatcgctctggcgcgctcagcgcggcgtttgcctaaatggagctcgccaacggcaagataggcaataattccgactaacggcagaatgtaaataatcaacagccaggccatcgcggagggaactgcgcgtcgtttcattagaatgcgtaaagttacgcctgcaatgagcaaccagtatcccagaatggccaaccaactcaccaacgtataaacggttgtcat
>KO_gene2
atgaataaaatcctgttagttgatgatgaccgagagctgacttattaaaggagctgctcgagatggaaggcttcaacgtgattgttgcccacgatggggaacaggcgcttgatcttctggacgacagcattgatttacttttgcttgacgtaatgatgccgaagaaaaatggtatcgacacattaaaagcacttcgccagacacaccagacgcctgtcattatgttgacggcgcgcggcagtgaacttgatcgcgttctcggccttgagctgggcgcagatgactatctcccgaaaccgtttaatgatcgtgagctggtggcacgtattcgcgcgatcctgcgccgttcgcactggagcgagcaacagcaaaacaacgacaacggttcaccgacactggaagttgatgccttagtgctgaatccaggccgtcaggaagccagcttcgacgggcaaacgctggagttaaccggtactgagtttaccctgctctatttgctggcacagcatctgggtcaggtggtttcccgtgaacatttaagccaggaagtgttgggcaaacgcctgacgcctttcgaccgcgctattgatatgcacatttccaacctgcgtcgtaaactgccggatcgtaaagatggtcacccgtggtttaaaaccttgcgtggtcgcggctatctgatggtttctgcttcatga
>KO_gene3
aatcagcccggtaataacggacaagaccgcgacccgtggggaagcagcaaacctggcggcaactctgagggaaatggaaacaaaggcggtcgcgatcaagggccacctgatttagatgatatcttccgcaaactgagcaaaaagctcggtggtctgggcggcggtaaaggcaccggatctggcggtggcagttcatcgcaaggcccgcgcccgcagcttggcggtcgtgtcgttaccatcgcagcggcagcgattgtcattatctgggcggccagtggtttctataccattaaagaagccgaacgcggcgtggtaacacgctttggtaaattcagccatctggttgagccgggtctgaactggaaaccgacgtttatcgacgaagtcaaaccggtgaacgtggaagccgtgcgtgaactggccgcttctggtgtgatgctgacgtcggacgagaacgtagtgcgcgttgagatgaacgtgcagtaccgcgtcaccaatccggaaaaatatctgtatagcgtgaccagcccggatgacagcctgcgtcaggctaccgacagcgccctgcgtggagttatcggtaaatacaccatggaccgcattctgacggaaggtcgtaccgtgattcgtagcgatactcagcgcgaactggaagagacgattcgtccgtatgacatgggtatcacgctgctggacgtcaacttccaggctgctcgtccgccggaagaagtaaaagcggcgtttgacgatgcgattgccgcgcgtgaaaacgaacagcaatacattcgtgaagcagaagcgtataccaacgaagttcagccgcgtgcgaacggtcaggcgcaacgtatcctcgaagaggcgcgtgcgtacaaggcccagaccatcctggaagctcagggtgaagtggcgcgctttgctaaacttctgccggaatataaagccgcgccggaaattactcgcgagcgtctgtatatcgagacgatggaaaaagtgttgggtaacacccgcaaagtgctggttaacgataaaggtggcaacctgatggttctgccgttagaccagatgctgaaaggtggtaacgcccctgcggcgaagagcgataacggtgccagcaatctgctgcgtctgccgccagcctcttcctccacaaccagtggagcaagcaacacgtcgtccaccagtcagggcgatattatggaccaacgccgcgccaacgcgcagcgtaacgactaccagcgtcagggggaataacgatgcgtaagtcagttatcgcgattatcatcatcgtgctggtagtgctttacatgtctgtctttgtcgtcaaagaaggtgagcgcggtattacgctgcgttttggtaaggtactgcgtgacgatgacaacaaacctctggtttatgagccgggtctgcatttcaagataccgttcattgaaacggtgaaaatgctcgacgcacgtattcagaccatggacaaccaggccgaccgctttgtgaccaaagagaagaaagacctgatcgtcgactcttacatcaaatggcgcatcagcgatttcagccgttactacctggcaacgggtggtggcgacatttcgcaagcggaagtgctgttgaaacgtaagttctctgaccgtctgcgttctgaaattggtcgcctggacgtgaaagatatcgtcaccgattcccgtggtcgtctgaccctcgaagtacgtgacgcgctgaactccggttctgcgggtacagaagatgaagttactaccccggcggcagataacgccattgccgaagcggcagagcgcgtaacggctgagacgaagggcaaagttccggtcatcaacccgaacagtatggcggcgctgggtattgaagttgtcgatgtgcgtatcaagcagatcaacctgccgaccgaagtgtctgaagcgatctacaaccgtatgcgcgccgagcgtgaagcggtagcgcgtcgtcaccgttcacaaggtcaggaagaagcggaaaaactgcgcgcgactgccgactatgaagtgaccagaacgctggcagaagctgagcgtcagggccgcatcatgcgtggtgaaggcgatgccgaagcagccaaactgtttgctgatgcattcagtaaagatccggacttctacgcattcatccgtagcctgcgtgcttatgagaacagcttctctggcaatcaggacgtgatggtcatgagcccggatagcgatttcttccgctacatgaagacgccgacttccgca
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Usage: relative_mapping_caculation_V2.0.py

options:
  -h, --help            show this help message and exit
  --MAPPING_REF FILENAME
                        the fasta filename you used for bwa mapping reference
  --TARGETS FILENAME    the fasta file includes all the tested gene sequences you want to measure
  --DEPTH_TAB FILENAME  the tab file describe the mapping depth of every base
  --CUTLEN DEFAULT 5000
                        the flanking seq length for target catulation part (extracted for model learning)
  --FIXLEN FIXLEN       flanking seq length you want to drop near the deletion edge location (default is 0)
  --OUT FILENAME        Output file name, report as default
  --DEV                 for developping and testing only normal user can just ignore this

Citation

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Acknowledgments