The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies
Identifieur interne : 000B51 ( Main/Exploration ); précédent : 000B50; suivant : 000B52The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies
Auteurs : Abdulkadir Elmas [États-Unis] ; Xiaodong Wang [États-Unis] ; Jacqueline M. Dresch [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Nucleotides, Transcription Factors.
- Binding Sites, Machine Learning, Support Vector Machine.
Abstract
Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped
Url:
DOI: 10.1371/journal.pone.0185570
PubMed: 28982128
PubMed Central: 5628859
Affiliations:
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Dresch</name><affiliation wicri:level="2"><nlm:aff id="aff002"><addr-line>Department of Mathematics and Computer Science, Clark University, Worcester, MA, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mathematics and Computer Science, Clark University, Worcester, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region></placeName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Binding Sites</term><term>Machine Learning</term><term>Nucleotides (metabolism)</term><term>Support Vector Machine</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage machine</term><term>Facteurs de transcription (métabolisme)</term><term>Machine à vecteur de support</term><term>Nucléotides (métabolisme)</term><term>Sites de fixation</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Nucleotides</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term><term>Nucléotides</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Binding Sites</term><term>Machine Learning</term><term>Support Vector Machine</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage machine</term><term>Machine à vecteur de support</term><term>Sites de fixation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped <italic>k</italic>-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early <italic>Drosophila</italic> embryos.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Borok, M" uniqKey="Borok M">M Borok</name></author><author><name sortKey="Tran, D" uniqKey="Tran D">D Tran</name></author><author><name sortKey="Ho, M" uniqKey="Ho M">M Ho</name></author><author><name sortKey="Ra, D" uniqKey="Ra D">D RA</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Starr, M" uniqKey="Starr M">M Starr</name></author><author><name sortKey="Ho, M" uniqKey="Ho M">M Ho</name></author><author><name sortKey="Gunther, E" uniqKey="Gunther E">E Gunther</name></author><author><name sortKey="Tu, Y" uniqKey="Tu Y">Y Tu</name></author><author><name sortKey="Shur, A" uniqKey="Shur A">A 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Dresch</name><name sortKey="Wang, Xiaodong" sort="Wang, Xiaodong" uniqKey="Wang X" first="Xiaodong" last="Wang">Xiaodong Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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