In silico detection of sequence variations modifying transcriptional regulation.

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded prote...

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Autores principales:	Malin C Andersen, Pär G Engström, Stuart Lithwick, David Arenillas, Per Eriksson, Boris Lenhard, Wyeth W Wasserman, Jacob Odeberg
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/75312311854f482284de8f3108b9ae78
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spelling	oai:doaj.org-article:75312311854f482284de8f3108b9ae782021-11-25T05:41:29ZIn silico detection of sequence variations modifying transcriptional regulation.1553-734X1553-735810.1371/journal.pcbi.0040005https://doaj.org/article/75312311854f482284de8f3108b9ae782008-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18208319/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.Malin C AndersenPär G EngströmStuart LithwickDavid ArenillasPer ErikssonBoris LenhardWyeth W WassermanJacob OdebergPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 1, p e5 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Malin C Andersen Pär G Engström Stuart Lithwick David Arenillas Per Eriksson Boris Lenhard Wyeth W Wasserman Jacob Odeberg In silico detection of sequence variations modifying transcriptional regulation.
description	Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.
format	article
author	Malin C Andersen Pär G Engström Stuart Lithwick David Arenillas Per Eriksson Boris Lenhard Wyeth W Wasserman Jacob Odeberg
author_facet	Malin C Andersen Pär G Engström Stuart Lithwick David Arenillas Per Eriksson Boris Lenhard Wyeth W Wasserman Jacob Odeberg
author_sort	Malin C Andersen
title	In silico detection of sequence variations modifying transcriptional regulation.
title_short	In silico detection of sequence variations modifying transcriptional regulation.
title_full	In silico detection of sequence variations modifying transcriptional regulation.
title_fullStr	In silico detection of sequence variations modifying transcriptional regulation.
title_full_unstemmed	In silico detection of sequence variations modifying transcriptional regulation.
title_sort	in silico detection of sequence variations modifying transcriptional regulation.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/75312311854f482284de8f3108b9ae78
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In silico detection of sequence variations modifying transcriptional regulation.

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