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

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 adv

SUBMITTER: Elmas A 

PROVIDER: S-EPMC5628859 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature