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Large-scale computational identification of regulatory SNPs with rSNP-MAPPER.

Riva A - BMC Genomics (2012)

Bottom Line: In this work we focus in particular on SNPs that potentially affect a Transcription Factor Binding Site (TFBS) to a significant extent, possibly resulting in changes to gene expression patterns or alternative splicing.The application described here is based on the MAPPER platform, a previously developed web-based system for the computational detection of TFBSs in DNA sequences. rSNP-MAPPER is a computational tool that analyzes SNPs lying within predicted TFBSs and determines whether the allele substitution results in a significant change in the TFBS predictive score.We then present several examples of the use of rSNP-MAPPER to reproduce and confirm experimental studies aimed at identifying regulatory SNPs in human genes, that show how rSNP-MAPPER is able to detect and characterize rSNPs with high accuracy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32610, USA. ariva@ufl.edu

ABSTRACT

Background: The computational analysis of regulatory SNPs (rSNPs) is an essential step in the elucidation of the structure and function of regulatory networks at the cellular level. In this work we focus in particular on SNPs that potentially affect a Transcription Factor Binding Site (TFBS) to a significant extent, possibly resulting in changes to gene expression patterns or alternative splicing. The application described here is based on the MAPPER platform, a previously developed web-based system for the computational detection of TFBSs in DNA sequences.

Methods: rSNP-MAPPER is a computational tool that analyzes SNPs lying within predicted TFBSs and determines whether the allele substitution results in a significant change in the TFBS predictive score. The application's simple and intuitive interface supports several usage modes. For example, the user may search for potential rSNPs in the promoters of one or more genes, specified as a list of identifiers or chosen among the members of a pathway. Alternatively, the user may specify a set of SNPs to be analyzed by uploading a list of SNP identifiers or providing the coordinates of a genomic region. Finally, the user can provide two alternative sequences (wildtype and mutant), and the system will determine the location of variants to be analyzed by comparing them.

Results: In this paper we outline the architecture of rSNP-MAPPER, describing its intuitive and powerful user interface in detail. We then present several examples of the use of rSNP-MAPPER to reproduce and confirm experimental studies aimed at identifying regulatory SNPs in human genes, that show how rSNP-MAPPER is able to detect and characterize rSNPs with high accuracy. Results are richly annotated and can be displayed online or downloaded in a number of different formats.

Conclusions: rSNP-MAPPER is optimized for large scale work, allowing for the efficient annotation of thousands of SNPs, and is designed to assist in the genome-wide investigation of transcriptional regulatory networks, prioritizing potential rSNPs for subsequent experimental validation. rSNP-MAPPER is freely available at http://genome.ufl.edu/mapper/.

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Related in: MedlinePlus

The page displaying the results of an rSNP-MAPPER run. The top part of the window displays run parameters and allows the user to control how results are displayed and exported. The bottom part contains the hit pairs produced by the program, one per line. Information shown for each hit pair includes the SNP identifier and position, the scores related to the two SNP alleles and their difference, the model that produced the predicted TFBS and the corresponding factor name, and the genomic coordinates of the TFBS.
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Figure 2: The page displaying the results of an rSNP-MAPPER run. The top part of the window displays run parameters and allows the user to control how results are displayed and exported. The bottom part contains the hit pairs produced by the program, one per line. Information shown for each hit pair includes the SNP identifier and position, the scores related to the two SNP alleles and their difference, the model that produced the predicted TFBS and the corresponding factor name, and the genomic coordinates of the TFBS.

Mentions: Clicking the mouse button over a row opens a box containing more exhaustive information about the hit pair in that row. Additional fields displayed in this case include information about the gene, if any (gene symbol, NCBI gene id, ENSEMBL gene id, mRNA accession), the alignments corresponding to the two SNP alleles, the TFBS position according to all three reference systems (absolute on chromosome, relative to transcript start, relative to ATG), and a flag indicating whether the hit pair lies in an evolutionarily conserved region. Moreover, several fields in this box are hyperlinks to pages with further information. For example, the Gene ID is linked to the NCBI Gene page for that gene; the model accession number is a link to the MAPPER page describing that model, and the absolute hit position is a link to the UCSC Genome Browser. Figure 2 shows a typical result page for a single-gene rSNP-MAPPER run.


Large-scale computational identification of regulatory SNPs with rSNP-MAPPER.

Riva A - BMC Genomics (2012)

The page displaying the results of an rSNP-MAPPER run. The top part of the window displays run parameters and allows the user to control how results are displayed and exported. The bottom part contains the hit pairs produced by the program, one per line. Information shown for each hit pair includes the SNP identifier and position, the scores related to the two SNP alleles and their difference, the model that produced the predicted TFBS and the corresponding factor name, and the genomic coordinates of the TFBS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3303742&req=5

Figure 2: The page displaying the results of an rSNP-MAPPER run. The top part of the window displays run parameters and allows the user to control how results are displayed and exported. The bottom part contains the hit pairs produced by the program, one per line. Information shown for each hit pair includes the SNP identifier and position, the scores related to the two SNP alleles and their difference, the model that produced the predicted TFBS and the corresponding factor name, and the genomic coordinates of the TFBS.
Mentions: Clicking the mouse button over a row opens a box containing more exhaustive information about the hit pair in that row. Additional fields displayed in this case include information about the gene, if any (gene symbol, NCBI gene id, ENSEMBL gene id, mRNA accession), the alignments corresponding to the two SNP alleles, the TFBS position according to all three reference systems (absolute on chromosome, relative to transcript start, relative to ATG), and a flag indicating whether the hit pair lies in an evolutionarily conserved region. Moreover, several fields in this box are hyperlinks to pages with further information. For example, the Gene ID is linked to the NCBI Gene page for that gene; the model accession number is a link to the MAPPER page describing that model, and the absolute hit position is a link to the UCSC Genome Browser. Figure 2 shows a typical result page for a single-gene rSNP-MAPPER run.

Bottom Line: In this work we focus in particular on SNPs that potentially affect a Transcription Factor Binding Site (TFBS) to a significant extent, possibly resulting in changes to gene expression patterns or alternative splicing.The application described here is based on the MAPPER platform, a previously developed web-based system for the computational detection of TFBSs in DNA sequences. rSNP-MAPPER is a computational tool that analyzes SNPs lying within predicted TFBSs and determines whether the allele substitution results in a significant change in the TFBS predictive score.We then present several examples of the use of rSNP-MAPPER to reproduce and confirm experimental studies aimed at identifying regulatory SNPs in human genes, that show how rSNP-MAPPER is able to detect and characterize rSNPs with high accuracy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32610, USA. ariva@ufl.edu

ABSTRACT

Background: The computational analysis of regulatory SNPs (rSNPs) is an essential step in the elucidation of the structure and function of regulatory networks at the cellular level. In this work we focus in particular on SNPs that potentially affect a Transcription Factor Binding Site (TFBS) to a significant extent, possibly resulting in changes to gene expression patterns or alternative splicing. The application described here is based on the MAPPER platform, a previously developed web-based system for the computational detection of TFBSs in DNA sequences.

Methods: rSNP-MAPPER is a computational tool that analyzes SNPs lying within predicted TFBSs and determines whether the allele substitution results in a significant change in the TFBS predictive score. The application's simple and intuitive interface supports several usage modes. For example, the user may search for potential rSNPs in the promoters of one or more genes, specified as a list of identifiers or chosen among the members of a pathway. Alternatively, the user may specify a set of SNPs to be analyzed by uploading a list of SNP identifiers or providing the coordinates of a genomic region. Finally, the user can provide two alternative sequences (wildtype and mutant), and the system will determine the location of variants to be analyzed by comparing them.

Results: In this paper we outline the architecture of rSNP-MAPPER, describing its intuitive and powerful user interface in detail. We then present several examples of the use of rSNP-MAPPER to reproduce and confirm experimental studies aimed at identifying regulatory SNPs in human genes, that show how rSNP-MAPPER is able to detect and characterize rSNPs with high accuracy. Results are richly annotated and can be displayed online or downloaded in a number of different formats.

Conclusions: rSNP-MAPPER is optimized for large scale work, allowing for the efficient annotation of thousands of SNPs, and is designed to assist in the genome-wide investigation of transcriptional regulatory networks, prioritizing potential rSNPs for subsequent experimental validation. rSNP-MAPPER is freely available at http://genome.ufl.edu/mapper/.

Show MeSH
Related in: MedlinePlus