Fast and sensitive mapping of nanopore sequencing reads with GraphMap

TLDR

Nanopore sequencing’s high error rates demand new bioinformatics methods to realize its democratic promise. We introduce GraphMap, a mapping algorithm that refines candidate alignments and uses fast graph traversal to align long reads with high precision (>95 %). GraphMap progressively refines alignments to robustly handle high‑error rates while efficiently traversing a graph for long‑read alignment. GraphMap improves mapping sensitivity by 10–80 %, maps over 95 % of bases, enables 15 % more sensitive SNP calling, accurately detects structural variants from 100 bp to 4 kbp, and identifies pathogens at species and strain level from MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.

Abstract

Realizing the democratic promise of nanopore sequencing requires the development of new bioinformatics approaches to deal with its specific error characteristics. Here we present GraphMap, a mapping algorithm designed to analyse nanopore sequencing reads, which progressively refines candidate alignments to robustly handle potentially high-error rates and a fast graph traversal to align long reads with speed and high precision (>95%). Evaluation on MinION sequencing data sets against short- and long-read mappers indicates that GraphMap increases mapping sensitivity by 10-80% and maps >95% of bases. GraphMap alignments enabled single-nucleotide variant calling on the human genome with increased sensitivity (15%) over the next best mapper, precise detection of structural variants from length 100 bp to 4 kbp, and species and strain-specific identification of pathogens using MinION reads. GraphMap is available open source under the MIT license at https://github.com/isovic/graphmap.