In silico detection of sequence variations modifying transcriptional regulation

• Published in: PLoS computational biology 2008-01, Vol.4 (1), p.e5-e5
• Main Authors: Andersen, Malin C, Engström, Pär G, Lithwick, Stuart, Arenillas, David, Eriksson, Per, Lenhard, Boris, Wasserman, Wyeth W, Odeberg, Jacob
• Format: Article
• Language: English
• Subjects: Algorithms; Binding Sites; Biochemistry; Biokemi; Chemistry; Computational Biology; Genetic Variation - genetics; Genetics; Genetics and Genomics; Genomics; Homo (Human); Internet; Kemi; Medical research; Medicin och hälsovetenskap; Mus (Mouse); Mutation; NATURAL SCIENCES; NATURVETENSKAP; Polymorphism, Single Nucleotide - genetics; Protein Binding; Proteins; Regulatory Elements, Transcriptional - genetics; Sequence Analysis, DNA - methods; Software; Transcription Factors - genetics
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.0040005

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.