STEC_KMA

STEC_KMA is a bioinformatics tool designed to process sequencing data for Shiga toxin-producing Escherichia coli (STEC). It uses the KMA (K-mer Alignment) algorithm to map sequencing reads to a combined allele database, identify the best hits, extract relevant reads, and confirm results through alignment using BWA (Burrows-Wheeler Aligner). The tool also identifies alleles with insertions and generates comprehensive reports.

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Features

• Read Baiting: Uses BBMap to bait reads from sequencing data based on a reference allele database.
• KMA Mapping: Maps reads to allele databases using KMA to identify the best hits.
• BWA Alignment: Aligns reads to reference alleles to confirm results and identify insertions.
• Consensus Sequence Extraction: Extracts consensus sequences and identifies insertions from mapped reads.
• Stx Profile Calculation: Calculates Shiga toxin (stx) profiles for each sample.
• Comprehensive Reporting: Generates detailed reports, including allele matches, percent identity, and insertion details.

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Installation

Step 1: Install Conda

Ensure you have Conda installed. If not, download and install Miniconda or Anaconda from https://docs.conda.io/en/latest/miniconda.html.

Step 2: Create the Conda Environment

Create a Conda environment named stec_kma and install the required dependencies:

conda create -n stec_kma olcbioinformatics::stec_kma
conda activate stec_kma

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Usage

Run the stec_kma.py script with the required arguments:

python src/stec_kma.py -s <sequence_path> -d <database_path> -r <report_path> -t <threads> -ID <identity> -c <min_coverage>

Arguments

• -s, --sequence_path: Path to the folder containing sequencing reads (required).
• -d, --database_path: Name and path of the indexed KMA database (required). This is the value provided to the -o argument when running the kma index command. Ensure that the database has been processed with kma index before running this tool.
• -r, --report_path: Path to the folder where reports will be written (optional, defaults to sequence_path/reports).
• -t, --threads: Number of threads to use (default: number of CPU cores).
• -ID, --identity: Minimum identity percentage for KMA hits (default: 90%).
• -c, --min_coverage: Minimum fraction of reads required to call a consensus insertion (default: 0.7).
• --verbosity: Logging verbosity level (DEBUG, INFO, WARNING, ERROR, CRITICAL; default: INFO).

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Pipeline Overview

The stec_kma.py script orchestrates the following steps:

1. Locate Samples:

   • Organizes FASTQ files into a dictionary and creates subdirectories for each sample.

2. Bait Reads:

   • Uses BBMap to bait reads from the input FASTQ files based on the reference allele database.

3. Reverse Bait Targets:

   • Baits targets from the allele database using the previously baited reads.

4. Index Baited Sequences:

   • Indexes the baited allele sequences using KMA.

5. Map Reads with KMA:

   • Maps reads to the indexed allele database using KMA.

6. Extract Allele Sequences:

   • Extracts allele sequences corresponding to the best hits from the KMA reports.

7. Map Reads with BWA:

   • Aligns reads to reference alleles using BWA to confirm results and identify insertions.

8. Extract Consensus Sequences:

   • Extracts consensus sequences and identifies insertions from the BWA output.

9. Calculate Stx Profiles:

   • Calculates Shiga toxin (stx) profiles for each sample.

10. Generate Reports:

    • Writes detailed reports summarizing the results.

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Output

The tool generates the following outputs:

1. Reports:

   • A tab-delimited report (stec_kma_report.tsv) summarizing:
     • Sample name
     • Best allele match
     • Percent identity
     • Stx profiles
     • Notes (e.g., insertions, truncations, or internal stop codons)

2. FASTA Files:

   • Nucleotide and amino acid sequences for alleles with insertions.

3. Intermediate Files:

   • Baited reads, mapped reads, and alignment files for further analysis.

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Example

Before running stec_kma.py, ensure that kma index has been run on your database. For example:

kma index -i /path/to/allele_db.fasta -o /path/to/indexed_db

Then, run the tool:

python src/stec_kma.py \
    -s /path/to/sequence_data \
    -d /path/to/indexed_db \
    -r /path/to/output_reports \
    -t 8 \
    -ID 95 \
    -c 0.8 \
    --verbosity DEBUG

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Logging

The tool supports configurable logging levels (DEBUG, INFO, WARNING, ERROR, CRITICAL). Logs include detailed information about each step of the pipeline, including system commands and outputs.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For questions or issues, please contact:

• Author: Adam Koziol
• Email: [email protected]