MIRUReader

MIRUReader performs in-silico MIRU-VNTR typing of the Mycobacterium tuberculosis complex (MTBC) using long-read sequencing data (Pacific Biosciences and Oxford Nanopore Technologies) to derive 24-locus VNTR patterns for epidemiological genotyping.


Key Features:

  • 24-locus MIRU-VNTR typing: Analyzes the 24 MIRU-VNTR loci specific to MTBC to determine locus-specific VNTR patterns.
  • Long-read compatibility: Directly analyzes long sequence reads from Pacific Biosciences and Oxford Nanopore Technologies to cover entire repeat regions within each locus.
  • Raw reads and assemblies: Processes both raw long reads and assembled genomes as input.
  • VNTR identification and analysis: Identifies and analyzes variable number tandem repeats (VNTRs) across MIRU loci for pattern determination.
  • Short-read limitation mitigation: Overcomes limitations of short-read sequencing (Illumina) by enabling repeat-region-spanning analyses.
  • Epidemiological genotyping output: Produces detailed MIRU-VNTR patterns for strain differentiation and transmission inference.

Scientific Applications:

  • Tuberculosis transmission tracking: Supports epidemiological studies that track MTBC transmission through VNTR-based genotyping.
  • Strain differentiation and surveillance: Enables differentiation of MTBC strains using 24-locus MIRU-VNTR patterns for surveillance and outbreak investigations.
  • Long-read VNTR analysis: Applied to long-read sequencing datasets (Pacific Biosciences, Oxford Nanopore Technologies) for comprehensive VNTR characterization.

Methodology:

Processes raw long reads and assembled genomes to identify and analyze variable number tandem repeats (VNTRs) across the 24 MIRU-VNTR loci of MTBC.

Topics

Details

License:
MIT
Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/9/2020
Last Updated:
12/29/2020

Operations

Publications

Tang CY, Ong RT. MIRUReader: MIRU-VNTR typing directly from long sequencing reads. Bioinformatics. 2019;36(5):1625-1626. doi:10.1093/bioinformatics/btz771. PMID:31603462.

PMID: 31603462
Funding: - Singapore Infectious Diseases Initiative: SIDI/2014/003