MersV1, PubMed, Corpus, bibRecord, 000837

Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.

Identifieur interne : 000837 ( PubMed/Corpus ); précédent : 000836; suivant : 000838

Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.

Auteurs : Yasunobu Okamura ; Kengo Kinoshita

Source :

BMC bioinformatics [ 1471-2105 ] ; 2018.

RBID : pubmed:30012088

English descriptors

KwdEn :
- Animals, Base Sequence, Computational Biology (methods), Computer Simulation, Genome, Humans, Mice, RNA, Messenger (analysis), Sequence Analysis, RNA (methods), Time Factors.
MESH :
- chemical , analysis : RNA, Messenger.
- methods : Computational Biology, Sequence Analysis, RNA.
- Animals, Base Sequence, Computer Simulation, Genome, Humans, Mice, Time Factors.

Abstract

Data generated by RNA sequencing (RNA-Seq) is now accumulating in vast amounts in public repositories, especially for human and mouse genomes. Reanalyzing these data has emerged as a promising approach to identify gene modules or pathways. Although meta-analyses of gene expression data are frequently performed using microarray data, meta-analyses using RNA-Seq data are still rare. This lag is partly due to the limitations in reanalyzing RNA-Seq data, which requires extensive computational resources. Moreover, it is nearly impossible to calculate the gene expression levels of all samples in a public repository using currently available methods. Here, we propose a novel method, Matataki, for rapidly estimating gene expression levels from RNA-Seq data.

DOI: 10.1186/s12859-018-2279-y
PubMed: 30012088

Links to Exploration step

pubmed:30012088

Le document en format XML

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<author><name sortKey="Okamura, Yasunobu" sort="Okamura, Yasunobu" uniqKey="Okamura Y" first="Yasunobu" last="Okamura">Yasunobu Okamura</name>
<affiliation><nlm:affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Kinoshita, Kengo" sort="Kinoshita, Kengo" uniqKey="Kinoshita K" first="Kengo" last="Kinoshita">Kengo Kinoshita</name>
<affiliation><nlm:affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.</nlm:affiliation>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.</title>
<author><name sortKey="Okamura, Yasunobu" sort="Okamura, Yasunobu" uniqKey="Okamura Y" first="Yasunobu" last="Okamura">Yasunobu Okamura</name>
<affiliation><nlm:affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.</nlm:affiliation>
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<author><name sortKey="Kinoshita, Kengo" sort="Kinoshita, Kengo" uniqKey="Kinoshita K" first="Kengo" last="Kinoshita">Kengo Kinoshita</name>
<affiliation><nlm:affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.</nlm:affiliation>
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<series><title level="j">BMC bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
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<term>Genome</term>
<term>Humans</term>
<term>Mice</term>
<term>RNA, Messenger (analysis)</term>
<term>Sequence Analysis, RNA (methods)</term>
<term>Time Factors</term>
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<keywords scheme="MESH" type="chemical" qualifier="analysis" xml:lang="en"><term>RNA, Messenger</term>
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<keywords scheme="MESH" xml:lang="en"><term>Animals</term>
<term>Base Sequence</term>
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<front><div type="abstract" xml:lang="en">Data generated by RNA sequencing (RNA-Seq) is now accumulating in vast amounts in public repositories, especially for human and mouse genomes. Reanalyzing these data has emerged as a promising approach to identify gene modules or pathways. Although meta-analyses of gene expression data are frequently performed using microarray data, meta-analyses using RNA-Seq data are still rare. This lag is partly due to the limitations in reanalyzing RNA-Seq data, which requires extensive computational resources. Moreover, it is nearly impossible to calculate the gene expression levels of all samples in a public repository using currently available methods. Here, we propose a novel method, Matataki, for rapidly estimating gene expression levels from RNA-Seq data.</div>
</front>
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<DateCompleted><Year>2019</Year>
<Month>04</Month>
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<DateRevised><Year>2020</Year>
<Month>03</Month>
<Day>06</Day>
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<Title>BMC bioinformatics</Title>
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<ArticleTitle>Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND">Data generated by RNA sequencing (RNA-Seq) is now accumulating in vast amounts in public repositories, especially for human and mouse genomes. Reanalyzing these data has emerged as a promising approach to identify gene modules or pathways. Although meta-analyses of gene expression data are frequently performed using microarray data, meta-analyses using RNA-Seq data are still rare. This lag is partly due to the limitations in reanalyzing RNA-Seq data, which requires extensive computational resources. Moreover, it is nearly impossible to calculate the gene expression levels of all samples in a public repository using currently available methods. Here, we propose a novel method, Matataki, for rapidly estimating gene expression levels from RNA-Seq data.</AbstractText>
<AbstractText Label="RESULTS">The proposed method uses k-mers that are unique to each gene for the mapping of fragments to genes. Since aligning fragments to reference sequences requires high computational costs, our method could reduce the calculation cost by focusing on k-mers that are unique to each gene and by skipping uninformative regions. Indeed, Matataki outperformed conventional methods with regards to speed while demonstrating sufficient accuracy.</AbstractText>
<AbstractText Label="CONCLUSIONS">The development of Matataki can overcome current limitations in reanalyzing RNA-Seq data toward improving the potential for discovering genes and pathways associated with disease at reduced computational cost. Thus, the main bottleneck of RNA-Seq analyses has shifted to achieving the decompression of sequenced data. The implementation of Matataki is available at https://github.com/informationsea/Matataki .</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Okamura</LastName>
<ForeName>Yasunobu</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Mitsubishi Space Software Co., Ltd, Amagasaki, Hyogo, Japan.</Affiliation>
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<AffiliationInfo><Affiliation>Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.</Affiliation>
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<AffiliationInfo><Affiliation>Tohoku Medical Megabank Organization, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.</Affiliation>
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<AffiliationInfo><Affiliation>Institute of Development, Tohoku University, Sendai, Miyagi, Japan. kengo@ecei.tohoku.ac.jp.</Affiliation>
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<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Gene expression</Keyword>
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Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.

Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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