Training alignment parameters for arbitrary sequencers with LAST-TRAIN

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

doi:10.1093/bioinformatics/btw742

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

School of Advanced Science and Engineering

Research output: Contribution to journal › Article › peer-review

38 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 language	English
Pages (from-to)	926-928
Number of pages	3
Journal	Bioinformatics
Volume	33
Issue number	6
DOIs	https://doi.org/10.1093/bioinformatics/btw742
Publication status	Published - 2017

ASJC Scopus subject areas

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

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10.1093/bioinformatics/btw742

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title = "Training alignment parameters for arbitrary sequencers with LAST-TRAIN",

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.",

author = "Michiaki Hamada and Yukiteru Ono and Kiyoshi Asai and Frith, {Martin C.} and John Hancock",

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AU - Hamada, Michiaki

AU - Ono, Yukiteru

AU - Asai, Kiyoshi

AU - Frith, Martin C.

AU - Hancock, John

N1 - Publisher Copyright: © The Author 2016.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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Training alignment parameters for arbitrary sequencers with LAST-TRAIN

Abstract

ASJC Scopus subject areas

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