Fast and sensitive mapping of nanopore sequencing reads with GraphMap

By Ivan Sovic, Mile Sikic, Andreas Wilm, Shannon N. Fenlon, S. Chen, Niranjan Nagarajan. Nature Communications, 2016

346 citations recorded by the source corpus.

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. Read mapping and alignment tools are critical for many applications based on MinION sequencers. Here, the authors present GraphMap, a mapping algorithm designed to analyze nanopore sequencing reads, that progressively refines candidate alignments to handle potentially high error rates to align long reads.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f082a5b6d04daec199b2be4d2bfeea36062af10

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Citations in the corpus

Nanopore sequencing technology, bioinformatics and applications (2021, methodology): “In 2016, the first aligner specifically for ONT reads, GraphMap, was developed 95 .”
De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing (2017, methodology): “For DNA this function has been resolved with considerable accuracy by Oxford Nanopore technologies, but significant errors remain in the 1D base calls (between 70%-90% accuracy reported though this depends strongly on the version of pore used 20, 21, 36 ).”
Fast and accurate de novo genome assembly from long uncorrected reads (2016, neutral): “Mappings can be generated using any mapper/overlapper that supportsthe MHAP, PAF, or SAM outputformats, such as minimap (Li 2016), MHAP (Berlin et al. 2015), or GraphMap (Sovic ́ et al. 2016b).”
Accurate detection of complex structural variations using single molecule sequencing (2017, neutral): “All simulated reads were mapped to the human reference genome (GRCh37) using BWA-mem 26 , BLASR 25 , GraphMap 27 , MECAT 28 , LAST 24 , Minimap2 29 , and NGMLR. Reads that overlap or map in close proximity to a simulated SV were extracted from the BAM files and used for evaluation.”
Minimap2: pairwise alignment for nucleotide sequences (2017, neutral): “We speculated there could be substantial room for speedup on the thought that 10kb long sequences should be easier to map than 100bp reads because we can more effectively skip repetitive regions, which are often the bottleneck of short-read alignment.”