MTBseq-nf: Enabling Scalable Tuberculosis Genomics "Big Data" Analysis Through a User-Friendly Nextflow Wrapper for...

Area of Science:

• Bioinformatics
• Genomic Surveillance
• Mycobacterium tuberculosis

Background:

• The original MTBseq pipeline (2018) addressed bioinformatics challenges in tuberculosis (TB) research using whole-genome sequencing (WGS).
• It was the first GitHub tool for comprehensive MTBC WGS analysis, including quality control, mapping, variant calling, lineage classification, drug resistance prediction, and phylogenetic inference.
• However, its linear architecture limited scalability on high-performance and cloud computing environments.

Purpose of the Study:

• To develop an optimized version of the MTBseq pipeline for enhanced performance and scalability.
• To introduce parallelization capabilities for faster WGS data analysis in TB research.
• To ensure reproducibility and platform independence for TB genomic surveillance.

Main Methods:

• Development of MTBseq-nf, a Nextflow wrapper for the MTBseq pipeline.
• Implementation of parallelization for simultaneous analysis instances.
• Benchmarking using 90 M. tuberculosis genomes on a dedicated computational server.
• Integration with nf-core, Bioconda, and Biocontainers best practices.

Main Results:

• MTBseq-nf demonstrated significantly faster execution speeds compared to the standard MTBseq pipeline.
• Parallel mode of MTBseq-nf was at least twice as fast for cohorts exceeding 20 samples.
• The wrapper ensures reproducibility and platform independence.

Conclusions:

• MTBseq-nf provides a scalable and efficient solution for TB genomic surveillance.
• The parallelized Nextflow wrapper overcomes the limitations of the original MTBseq pipeline.
• This advancement facilitates faster and more robust analysis of Mycobacterium tuberculosis WGS data.