NGMLR
NGMLR maps third-generation long-read sequencing data from PacBio and Oxford Nanopore to a reference genome to enable accurate alignment of reads that span structural variations.
Key Features:
- Long-read alignment: Aligns PacBio and Oxford Nanopore reads to reference genomes with emphasis on reads spanning structural variations.
- High sensitivity and precision: Detects structural variants, including events in repeat-rich regions and complex nested variants.
- Error management: Implements error correction and filtering mechanisms to reduce false-positive variant calls from high-error-rate long reads.
- Low-coverage compatibility: Operates effectively on low-coverage long-read datasets to support variant detection with reduced sequencing depth.
- Integration with Sniffles: Interfaces with the Sniffles structural variant caller for downstream SV identification.
Scientific Applications:
- Structural variant discovery: Enables discovery of novel structural variants that are difficult to detect with short-read sequencing.
- Human and cancer genomics: Applied to healthy and cancerous human genomes to identify large numbers of previously undetected variants.
- Evaluation of short-read limitations: Helps categorize systematic errors inherent in short-read approaches to improve understanding of genomic structure.
- Low-coverage studies: Supports structural variant analysis in low-coverage long-read sequencing experiments.
Methodology:
Employs alignment algorithms tailored for long-read sequencing and applies error correction and filtering mechanisms to accurately map reads that span structural variations.
Topics
Details
- License:
- MIT
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- C++
- Added:
- 5/15/2018
- Last Updated:
- 11/24/2024
Operations
Publications
Sedlazeck FJ, Rescheneder P, Smolka M, Fang H, Nattestad M, von Haeseler A, Schatz MC. Accurate detection of complex structural variations using single-molecule sequencing. Nature Methods. 2018;15(6):461-468. doi:10.1038/s41592-018-0001-7. PMID:29713083. PMCID:PMC5990442.
Documentation
General
https://github.com/philres/ngmlrA Github page with source code, documentation and test datasets.
Downloads
- Source codehttps://github.com/philres/ngmlrA Github page with source code, documentation and test datasets.