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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

Identifieur interne : 000B30 ( PubMed/Corpus ); précédent : 000B29; suivant : 000B31

The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

Auteurs : Abdulkadir Elmas ; Xiaodong Wang ; Jacqueline M. Dresch

Source :

PloS one [ 1932-6203 ] ; 2017.

RBID : pubmed:28982128

English descriptors

KwdEn :
- Binding Sites, Machine Learning, Nucleotides (metabolism), Support Vector Machine, Transcription Factors (metabolism).
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 k-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 Drosophila embryos.

DOI: 10.1371/journal.pone.0185570
PubMed: 28982128

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pubmed:28982128

Le document en format XML

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<author><name sortKey="Wang, Xiaodong" sort="Wang, Xiaodong" uniqKey="Wang X" first="Xiaodong" last="Wang">Xiaodong Wang</name>
<affiliation><nlm:affiliation>Department of Electrical Engineering, Columbia University, New York, NY, United States of America.</nlm:affiliation>
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<author><name sortKey="Dresch, Jacqueline M" sort="Dresch, Jacqueline M" uniqKey="Dresch J" first="Jacqueline M" last="Dresch">Jacqueline M. Dresch</name>
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<front><div type="abstract" xml:lang="en">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 k-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 Drosophila embryos.</div>
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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies.

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