Limits...
In silico detection of sequence variations modifying transcriptional regulation.

Andersen MC, Engström PG, Lithwick S, Arenillas D, Eriksson P, Lenhard B, Wasserman WW, Odeberg J - PLoS Comput. Biol. (2007)

Bottom Line: Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription.The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs.The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers).

View Article: PubMed Central - PubMed

Affiliation: Department of Gene Technology, School of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), Stockholm, Sweden.

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

Overview of the RAVEN Web Interface(A) The search page.(B) The search results page where a list of genes corresponding to the search query is displayed.(C) The reference sequence selection page where the genomic location of the selected human sequence and cDNAs that map to it is displayed.(D) The graphical results view.(E) Table view of SNPs predicted to affect TFBSs.(F) Selection of TFBS profiles from the JASPAR database. (G) Upload of private SNP sequences.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2211530&req=5

pcbi-0040005-g007: Overview of the RAVEN Web Interface(A) The search page.(B) The search results page where a list of genes corresponding to the search query is displayed.(C) The reference sequence selection page where the genomic location of the selected human sequence and cDNAs that map to it is displayed.(D) The graphical results view.(E) Table view of SNPs predicted to affect TFBSs.(F) Selection of TFBS profiles from the JASPAR database. (G) Upload of private SNP sequences.

Mentions: To facilitate efficient analyses of the type presented in this paper, computational methods and newly implemented algorithms were developed as an integrated framework for rSNP analysis. The framework includes all the components for the location and extraction of data from genome and SNP databases, pattern detection, phylogenetic footprinting, and SNP effect estimation. To facilitate user access, we developed a flexible Web application that enables researchers to easily detect potential regulatory gene variation in their gene of interest (http://www.cisreg.ca). The organizational schema of the Web application is shown in Figure 7. The progression through analysis in RAVEN is event-driven, i.e., designed for a number of different application scenarios using different subcomponents of the system, and in different order. The components are:


In silico detection of sequence variations modifying transcriptional regulation.

Andersen MC, Engström PG, Lithwick S, Arenillas D, Eriksson P, Lenhard B, Wasserman WW, Odeberg J - PLoS Comput. Biol. (2007)

Overview of the RAVEN Web Interface(A) The search page.(B) The search results page where a list of genes corresponding to the search query is displayed.(C) The reference sequence selection page where the genomic location of the selected human sequence and cDNAs that map to it is displayed.(D) The graphical results view.(E) Table view of SNPs predicted to affect TFBSs.(F) Selection of TFBS profiles from the JASPAR database. (G) Upload of private SNP sequences.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2211530&req=5

pcbi-0040005-g007: Overview of the RAVEN Web Interface(A) The search page.(B) The search results page where a list of genes corresponding to the search query is displayed.(C) The reference sequence selection page where the genomic location of the selected human sequence and cDNAs that map to it is displayed.(D) The graphical results view.(E) Table view of SNPs predicted to affect TFBSs.(F) Selection of TFBS profiles from the JASPAR database. (G) Upload of private SNP sequences.
Mentions: To facilitate efficient analyses of the type presented in this paper, computational methods and newly implemented algorithms were developed as an integrated framework for rSNP analysis. The framework includes all the components for the location and extraction of data from genome and SNP databases, pattern detection, phylogenetic footprinting, and SNP effect estimation. To facilitate user access, we developed a flexible Web application that enables researchers to easily detect potential regulatory gene variation in their gene of interest (http://www.cisreg.ca). The organizational schema of the Web application is shown in Figure 7. The progression through analysis in RAVEN is event-driven, i.e., designed for a number of different application scenarios using different subcomponents of the system, and in different order. The components are:

Bottom Line: Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription.The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs.The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers).

View Article: PubMed Central - PubMed

Affiliation: Department of Gene Technology, School of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), Stockholm, Sweden.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus