Training alignment parameters for arbitrary sequencers with LAST-TRAIN

Michiaki Hamada, Yukiteru Ono, Kiyoshi Asai, Martin C. Frith, John Hancock

    Research output: Contribution to journalArticle

    12 Citations (Scopus)

    Abstract

    LAST-TRAIN improves sequence alignment accuracy by inferring substitution and gap scores that fit the frequencies of substitutions, insertions, and deletions in a given dataset. We have applied it to mapping DNA reads from IonTorrent and PacBio RS, and we show that it reduces reference bias for Oxford Nanopore reads.

    Original languageEnglish
    Pages (from-to)926-928
    Number of pages3
    JournalBioinformatics
    Volume33
    Issue number6
    DOIs
    Publication statusPublished - 2017

    Fingerprint

    Nanopores
    Sequence Alignment
    Substitution
    Alignment
    Substitution reactions
    Nanopore
    DNA
    Arbitrary
    Deletion
    Insertion
    Training
    Datasets

    ASJC Scopus subject areas

    • Statistics and Probability
    • Medicine(all)
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

    Cite this

    Training alignment parameters for arbitrary sequencers with LAST-TRAIN. / Hamada, Michiaki; Ono, Yukiteru; Asai, Kiyoshi; Frith, Martin C.; Hancock, John.

    In: Bioinformatics, Vol. 33, No. 6, 2017, p. 926-928.

    Research output: Contribution to journalArticle

    Hamada, Michiaki ; Ono, Yukiteru ; Asai, Kiyoshi ; Frith, Martin C. ; Hancock, John. / Training alignment parameters for arbitrary sequencers with LAST-TRAIN. In: Bioinformatics. 2017 ; Vol. 33, No. 6. pp. 926-928.
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