Training alignment parameters for arbitrary sequencers with LAST-TRAIN

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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
Issue number6
Publication statusPublished - 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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