Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

BMC Bioinformatics

Research Article

Jesus Cerquides¹  Martin Nettling²  Ivo Grosse³  Hendrik Treutler⁴ 

[1] Institut d’Investigació en Intel ·ligència Artificial, IIIA-CSIC, Campus UAB, Cerdanyola, Spain;Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany;Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany;German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany;Leibniz Institute of Plant Biochemistry, Halle, Germany;

关键词: ChIP-Seq;    Phylogenetic footprinting;    Evolution;    Transcription factor binding sites;    Gene regulation;   

DOI  :  10.1186/s12859-017-1495-1

 received in 2016-06-29, accepted in 2017-01-24,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

【 授权许可】

CC BY   
© The Author(s) 2017

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