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Naruki Yoshikawa

dblp:228/5053 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0003-1546-8709ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
sequence analysis
0.312018
Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018
Bioinformatics and computational biology › sequence analysis › read mapping
split-read alignment
0.312018
Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018
Bioinformatics and computational biology › genomics
variant calling
0.112018
Combining probabilistic alignments with read pair information improves accuracy of split-alignments · Bioinform. 2018

Methods — techniques the papers use, named apart from their topics

probabilistic alignment · 0.3paired-end read information · 0.3
YearPublicationVenuePosition
2018 Combining probabilistic alignments with read pair information improves accuracy of split-alignments
abstract
Motivation: Split-alignments provide base-pair-resolution evidence of genomic rearrangements. In practice, they are found by first computing high-scoring local alignments, parts of which are then combined into a split-alignment. This approach is challenging when aligning a short read to a large and repetitive reference, as it tends to produce many spurious local alignments leading to ambiguities in identifying the correct split-alignment. This problem is further exacerbated by the fact that rearrangements tend to occur in repeat-rich regions. Results: We propose a split-alignment technique that combats the issue of ambiguous alignments by combining information from probabilistic alignment with positional information from paired-end reads. We demonstrate that our method finds accurate split-alignments, and that this translates into improved performance of variant-calling tools that rely on split-alignments. Availability and implementation: An open-source implementation is freely available at: https://bitbucket.org/splitpairedend/last-split-pe. Supplementary information: Supplementary data are available at Bioinformatics online.
Anish Man Singh Shrestha, Naruki Yoshikawa, Kiyoshi Asai
Bioinform.2