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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Amplicon Sequencing using the Long-Read Sequencing Technologies
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MTBseq-nf: Enabling Scalable Tuberculosis Genomics "Big Data" Analysis Through a User-Friendly Nextflow Wrapper for

Abhinav Sharma1, Davi Josué Marcon2,3, Johannes Loubser1

  • 1SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.

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Summary

A new bioinformatics tool, MTBseq-nf, enhances tuberculosis whole-genome sequencing analysis. This Nextflow wrapper offers parallelization for faster results, improving genomic surveillance efficiency.

Keywords:
MTBseqMycobacterium tuberculosisNextflowbioinformatics pipelinegenomic surveillancetuberculosis genomicswhole-genome sequencingworkflow

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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.