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