Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

Martin Nettling,Jesus Cerquides,Ivo Grosse,Hendrik Treutler

doi:10.1186/s12859-017-1495-1

Martin Nettling, Jesus Cerquides + Show 2 more

Open Access

https://doi.org/10.1186/s12859-017-1495-1

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Abstract

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.

Highlights

Transcriptional 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
In the first subsection we study if the proposed phylogenetic footprinting model (PFM) can capture intra-motif dependencies of orders 1 and 2 in ChIP-Seq data of 35 transcription factor (TF)
Intra-motif dependencies can be captured by phylogenetic footprinting In this subsection we study to which degree intra-motif dependencies can be captured using the PFMs of orders 1 and 2

Summary

Introduction

Transcriptional 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. 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. The first group comprises approaches based on sequences of only one species, which we refer to as onespecies approaches in this work, using statistical models for the binding of TFs to their TFBSs. One of the most popular motif models is the simple position weight matrix (PWM) model, which does not take into account any dependency between different positions of the same TFBS, but there are more complex motif models that. Irrespective of the wide variety of different motif models used, all of these approaches have in common that they do not take into account phylogenetic information available from orthologous sequences of phylogenetically related species. Examples for highly popular tools that model intra-motif dependencies are Dimont [14], MEME-ChIP [15], DeepBind [16], and diChIPMunk [17]

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