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.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
This work was supported by the IMaGIN platform (project no. 102 101 0025), through a grant from the Science and Engineering Research Council, funding to the Genome Institute of Singapore from the Agency for Science, Technology and Research (A*STAR), Singapore, and funding from the Croatian Science Foundation (Project no. 7353—Algorithms for Genome Sequence Analysis).