SCS: signal, context, and structure features for genome-wide human promoter recognition.
Identifieur interne : 005381 ( Main/Exploration ); précédent : 005380; suivant : 005382SCS: signal, context, and structure features for genome-wide human promoter recognition.
Auteurs : Jia Zeng [République populaire de Chine] ; Xiao-Yu Zhao ; Xiao-Qin Cao ; Hong YanSource :
- IEEE/ACM transactions on computational biology and bioinformatics [ 1557-9964 ]
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : CpG Islands, Promoter Regions, Genetic.
- methods : Genomics, Sequence Analysis, DNA.
- Algorithms, Gene Expression Regulation, Genome, Human, Humans, Societies, Scientific.
Abstract
This paper integrates the signal, context, and structure features for genome-wide human promoter recognition, which is important in improving genome annotation and analyzing transcriptional regulation without experimental supports of ESTs, cDNAs, or mRNAs. First, CpG islands are salient biological signals associated with approximately 50 percent of mammalian promoters. Second, the genomic context of promoters may have biological significance, which is based on n-mers (sequences of n bases long) and their statistics estimated from training samples. Third, sequence-dependent DNA flexibility originates from DNA 3D structures and plays an important role in guiding transcription factors to the target site in promoters. Employing decision trees, we combine above signal, context, and structure features to build a hierarchical promoter recognition system called SCS. Experimental results on controlled data sets and the entire human genome demonstrate that SCS is significantly superior in terms of sensitivity and specificity as compared to other state-of-the-art methods. The SCS promoter recognition system is available online as supplemental materials for academic use and can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.95.
DOI: 10.1109/TCBB.2008.95
PubMed: 20671324
Affiliations:
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last="Yan">Hong Yan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="????"><PubDate><MedlineDate>2010 Jul-Sep</MedlineDate></PubDate></date><idno type="RBID">pubmed:20671324</idno><idno type="pmid">20671324</idno><idno type="doi">10.1109/TCBB.2008.95</idno><idno type="wicri:Area/PubMed/Corpus">001F43</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001F43</idno><idno type="wicri:Area/PubMed/Curation">001F43</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001F43</idno><idno type="wicri:Area/PubMed/Checkpoint">002A79</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">002A79</idno><idno type="wicri:Area/Ncbi/Merge">000766</idno><idno type="wicri:Area/Ncbi/Curation">000766</idno><idno type="wicri:Area/Ncbi/Checkpoint">000766</idno><idno type="wicri:Area/Main/Merge">005461</idno><idno type="wicri:Area/Main/Curation">005381</idno><idno 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sortKey="Yan, Hong" sort="Yan, Hong" uniqKey="Yan H" first="Hong" last="Yan">Hong Yan</name></author></analytic><series><title level="j">IEEE/ACM transactions on computational biology and bioinformatics</title><idno type="eISSN">1557-9964</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>CpG Islands (genetics)</term><term>Gene Expression Regulation</term><term>Genome, Human</term><term>Genomics (methods)</term><term>Humans</term><term>Promoter Regions, Genetic (genetics)</term><term>Sequence Analysis, DNA (methods)</term><term>Societies, Scientific</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN ()</term><term>Génome humain</term><term>Génomique ()</term><term>Humains</term><term>Ilots CpG (génétique)</term><term>Régions promotrices (génétique) (génétique)</term><term>Régulation de l'expression des gènes</term><term>Sociétés savantes</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>CpG Islands</term><term>Promoter Regions, Genetic</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Ilots CpG</term><term>Régions promotrices (génétique)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Gene Expression Regulation</term><term>Genome, Human</term><term>Humans</term><term>Societies, Scientific</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de séquence d'ADN</term><term>Génome humain</term><term>Génomique</term><term>Humains</term><term>Régulation de l'expression des gènes</term><term>Sociétés savantes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper integrates the signal, context, and structure features for genome-wide human promoter recognition, which is important in improving genome annotation and analyzing transcriptional regulation without experimental supports of ESTs, cDNAs, or mRNAs. First, CpG islands are salient biological signals associated with approximately 50 percent of mammalian promoters. Second, the genomic context of promoters may have biological significance, which is based on n-mers (sequences of n bases long) and their statistics estimated from training samples. Third, sequence-dependent DNA flexibility originates from DNA 3D structures and plays an important role in guiding transcription factors to the target site in promoters. Employing decision trees, we combine above signal, context, and structure features to build a hierarchical promoter recognition system called SCS. Experimental results on controlled data sets and the entire human genome demonstrate that SCS is significantly superior in terms of sensitivity and specificity as compared to other state-of-the-art methods. The SCS promoter recognition system is available online as supplemental materials for academic use and can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.95.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><noCountry><name sortKey="Cao, Xiao Qin" sort="Cao, Xiao Qin" uniqKey="Cao X" first="Xiao-Qin" last="Cao">Xiao-Qin Cao</name><name sortKey="Yan, Hong" sort="Yan, Hong" uniqKey="Yan H" first="Hong" last="Yan">Hong Yan</name><name sortKey="Zhao, Xiao Yu" sort="Zhao, Xiao Yu" uniqKey="Zhao X" first="Xiao-Yu" last="Zhao">Xiao-Yu Zhao</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Zeng, Jia" sort="Zeng, Jia" uniqKey="Zeng J" first="Jia" last="Zeng">Jia Zeng</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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