Identifying splicing regulatory elements with de Bruijn graphs.
Identifieur interne : 001880 ( PubMed/Checkpoint ); précédent : 001879; suivant : 001881Identifying splicing regulatory elements with de Bruijn graphs.
Auteurs : Eman Badr [États-Unis] ; Lenwood S. HeathSource :
- Journal of computational biology : a journal of computational molecular cell biology [ 1557-8666 ] ; 2014.
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Abstract
Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. Here, we propose a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In our approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). As a result, we identify 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. Our model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation.
DOI: 10.1089/cmb.2014.0183
PubMed: 25393830
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Heath</name></author></analytic><series><title level="j">Journal of computational biology : a journal of computational molecular cell biology</title><idno type="eISSN">1557-8666</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Alternative Splicing (genetics)</term><term>Base Sequence</term><term>Computational Biology</term><term>Exons (genetics)</term><term>Genome</term><term>Humans</term><term>Introns</term><term>RNA Splicing (genetics)</term><term>Regulatory Sequences, Nucleic Acid (genetics)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Biologie informatique</term><term>Exons (génétique)</term><term>Génome</term><term>Humains</term><term>Introns</term><term>Séquence nucléotidique</term><term>Séquences d'acides nucléiques régulatrices (génétique)</term><term>Épissage alternatif (génétique)</term><term>Épissage des ARN (génétique)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Alternative Splicing</term><term>Exons</term><term>RNA Splicing</term><term>Regulatory Sequences, Nucleic Acid</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Exons</term><term>Séquences d'acides nucléiques régulatrices</term><term>Épissage alternatif</term><term>Épissage des ARN</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Base Sequence</term><term>Computational Biology</term><term>Genome</term><term>Humans</term><term>Introns</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Biologie informatique</term><term>Génome</term><term>Humains</term><term>Introns</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. Here, we propose a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In our approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). As a result, we identify 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. Our model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25393830</PMID><DateCompleted><Year>2015</Year><Month>07</Month><Day>14</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1557-8666</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>12</Issue><PubDate><Year>2014</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Journal of computational biology : a journal of computational molecular cell biology</Title><ISOAbbreviation>J. Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>Identifying splicing regulatory elements with de Bruijn graphs.</ArticleTitle><Pagination><MedlinePgn>880-97</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1089/cmb.2014.0183</ELocationID><Abstract><AbstractText>Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. Here, we propose a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In our approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). As a result, we identify 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. Our model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Badr</LastName><ForeName>Eman</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Computer Science, Virginia Tech , Blacksburg, Virginia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Heath</LastName><ForeName>Lenwood S</ForeName><Initials>LS</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Comput 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Heath</name></noCountry><country name="États-Unis"><region name="Virginie"><name sortKey="Badr, Eman" sort="Badr, Eman" uniqKey="Badr E" first="Eman" last="Badr">Eman Badr</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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