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Takehiro Hashimoto

dblp:85/7392 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics		 0.112009
Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite · Bioinform. 2009
Bioinformatics and computational biology › sequence analysis › read mapping
multi-mapping read resolution		 0.112009
Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite · Bioinform. 2009
Bioinformatics and computational biology › sequence analysis
read mapping		 0.112009
Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite · Bioinform. 2009
Bioinformatics and computational biology
next-generation sequencing		 0.012009
Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite · Bioinform. 2009

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

probabilistic assignment · 0.1
YearPublicationVenuePosition
2009 Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite
abstract
UNLABELLED: Multi-mapping sequence tags are a significant impediment to short-read sequencing platforms. These tags are routinely omitted from further analysis, leading to experimental bias and reduced coverage. Here, we present MuMRescueLite, a low-resource requirement version of the MuMRescue software that has been used by several next generation sequencing projects to probabilistically reincorporate multi-mapping tags into mapped short read data. AVAILABILITY AND IMPLEMENTATION: MuMRescueLite is written in Python; executables and documentation are available from http://genome.gsc.riken.jp/osc/english/software/.
Takehiro Hashimoto, Michiel J. L. de Hoon, Sean M. Grimmond, Carsten O. Daub, Yoshihide Hayashizaki, Geoffrey J. Faulkner
 Bioinform.1