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Riva A. Large-scale computational identification of regulatory SNPs with rSNP-MAPPER. BMC Genomics. 2012;13:S7doi: 10.1186/1471-2164-13-S4-S7.
Riva, A. (2012). Large-scale computational identification of regulatory SNPs with rSNP-MAPPER. BMC genomics, 13S7. https://doi.org/10.1186/1471-2164-13-S4-S7
Riva, Alberto. "Large-scale computational identification of regulatory SNPs with rSNP-MAPPER." BMC genomics vol. 13 (2012): S7. doi: https://doi.org/10.1186/1471-2164-13-S4-S7
Riva A. Large-scale computational identification of regulatory SNPs with rSNP-MAPPER. BMC Genomics. 2012 Jun 18;13:S7. doi: 10.1186/1471-2164-13-S4-S7. PMID: 22759655; PMCID: PMC3303742.
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BMC Genomics. 2012 Jun 18;13:S7. doi: 10.1186/1471-2164-13-S4-S7.

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

BMC genomics

Alberto Riva

Affiliations

  1. Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32610, USA. [email protected]

PMID: 22759655 PMCID: PMC3303742 DOI: 10.1186/1471-2164-13-S4-S7

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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