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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.

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Fractions of Regulatory and Background SNPs Overlapping Predicted TFBSsSNPs were analyzed using all transcription factors in the JASPAR database, and using TFBS score delta thresholds between one and nine.
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pcbi-0040005-g002: Fractions of Regulatory and Background SNPs Overlapping Predicted TFBSsSNPs were analyzed using all transcription factors in the JASPAR database, and using TFBS score delta thresholds between one and nine.

Mentions: To assess the applicability of the approach to real sequences, we tested the PWM models of TFBSs on instances of genetic variation shown experimentally to affect the regulation of their corresponding genes. We compiled a list of 104 examples of experimentally verified regulatory 1 bp substitution polymorphisms. For comparison, we also compiled a list of 4,000 background 1 bp substitutions from dbSNP with a minor allele frequency exceeding 0.05. We tested all SNPs for overlap with the TFBS models in the JASPAR database, and recorded the score deltas that fulfilled the criteria described in Methods. We then calculated the fractions of SNPs that were retained for various TFBS score delta thresholds. Figure 2 shows the fractions of retained regulatory and background SNPs for the score delta thresholds of 1 unit increments between one and nine.


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)

Fractions of Regulatory and Background SNPs Overlapping Predicted TFBSsSNPs were analyzed using all transcription factors in the JASPAR database, and using TFBS score delta thresholds between one and nine.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-0040005-g002: Fractions of Regulatory and Background SNPs Overlapping Predicted TFBSsSNPs were analyzed using all transcription factors in the JASPAR database, and using TFBS score delta thresholds between one and nine.
Mentions: To assess the applicability of the approach to real sequences, we tested the PWM models of TFBSs on instances of genetic variation shown experimentally to affect the regulation of their corresponding genes. We compiled a list of 104 examples of experimentally verified regulatory 1 bp substitution polymorphisms. For comparison, we also compiled a list of 4,000 background 1 bp substitutions from dbSNP with a minor allele frequency exceeding 0.05. We tested all SNPs for overlap with the TFBS models in the JASPAR database, and recorded the score deltas that fulfilled the criteria described in Methods. We then calculated the fractions of SNPs that were retained for various TFBS score delta thresholds. Figure 2 shows the fractions of retained regulatory and background SNPs for the score delta thresholds of 1 unit increments between one and nine.

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