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/ngmlr
A Github page with source code, documentation and test datasets.

Downloads