- Ergebnisse als:
- Druckansicht
- Endnote (RIS)
- BibTeX
- Tabelle: CSV | HTML
Publikation
Leitbild und Forschungsprofil
Molekulare Signalverarbeitung
Natur- und Wirkstoffchemie
Biochemie pflanzlicher Interaktionen
Stoffwechsel- und Zellbiologie
Unabhängige Nachwuchsgruppen
Program Center MetaCom
Publikationen
Gute Wissenschaftliche Praxis
Forschungsförderung
Netzwerke und Verbundprojekte
Symposien und Kolloquien
Alumni-Forschungsgruppen
Publikationen
Publikation
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.