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Title

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

AuthorsNettling, Martin; Treutler, Hendrik; Cerquides, Jesús ; Grosse, Ivo
KeywordsChIP-Seq
Phylogenetic footprinting
Evolution
Transcription factor binding sites
Gene regulation
Issue Date1-Mar-2017
PublisherBioMed Central
CitationBMC Bioinformatics 18(1): 141 (2017)
Abstract[ Background] 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. 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.
[Results] Here, 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.
[Conclusion] Combining 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.
Publisher version (URL)http://dx.doi.org/10.1186/s12859-017-1495-1
URIhttp://hdl.handle.net/10261/145968
DOI10.1186/s12859-017-1495-1
ISSN1471-2105
Appears in Collections:(IIIA) Artículos
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