Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1495-1) contains supplementary material, which is available to authorized users.

Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites

“…As an outlook, the method proposed here is of a general significance, and it will be in the future also implemented in the more complicated case of direct target prediction for eukaryotic transcriptional regulators. Moreover, while the model was here applied in the context of PSWM, more complex models which take into account interdependences of nucleotides in TFBS were also developed ( Eggeling et al, 2015 ; Kulakovskiy et al, 2016 ; Nettling et al, 2017 ). While these methods lead to a better performance in some cases, more often (simpler) PSWMs perform better, which is likely due to overfitting, i.e., due to a limited number of TFBS from which the model is trained ( Benos et al, 2002 ; Nguyen and Androulakis, 2009 ).…”

Unrealistic phylogenetic trees may improve phylogenetic footprinting

“…Phylogenetic footprinting improves the de-novo motif discovery by incorporating phylogenetic dependencies within the MSA in contrast to approaches based on sequences from only one species. Substitution models of DNA sequence evolution such as the F81 model ( Felsenstein, 1981 ) have been adapted to model the evolution of TFBSs in a position-specific manner, and it has been shown that these adapted models, which we call phylogenetic footprinting models (PFMs) for brevity, can detect TFBSs more accurately than models that neglect phylogenetic dependencies ( Clark et al , 2007 ; Gertz et al , 2006 ; Hardison and Taylor, 2012 ; Hawkins et al , 2009 ; Moses et al , 2004a ; Nettling et al , 2017 ).…”