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  Incorporating evolution of transcription factor binding sites into annotated alignments.

Bais, A. S., Grossmann, S., & Vingron, M. (2007). Incorporating evolution of transcription factor binding sites into annotated alignments. Journal of Bioscience, 32(Suppl. 1), 841-850. doi:10.1007/s12038-007-0084-2.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8198-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8199-B
Genre: Journal Article
Alternative Title : J. Biosci.

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 Creators:
Bais, Abha Singh1, Author
Grossmann, Steffen1, Author
Vingron, Martin2, Author              
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1Max Planck Society, ou_persistent13              
2Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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Free keywords: Alignments - evolutionary models - transcription factor binding sites
 Abstract: Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield “conserved TFBSs”. Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits) are generated. Moreover, the pair-profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions, as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs, we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this. Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.

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Language(s): eng - English
 Dates: 2007-08-01
 Publication Status: Published in print
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Title: Journal of Bioscience
  Alternative Title : J. Biosci.
Source Genre: Journal
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Pages: - Volume / Issue: 32 (Suppl. 1) Sequence Number: - Start / End Page: 841 - 850 Identifier: ISSN: 0250-5991