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

Alberto Riva

doi:10.1186/1471-2164-13-s4-s7

Abstract

BackgroundThe 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.MethodsrSNP-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.ResultsIn 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.ConclusionsrSNP-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/.

Highlights

Single nucleotide polymorphisms (SNPs) have become the genetic marker of choice for studies aimed at linking genotype and phenotype, thanks to the availability of a very large number of well-characterized SNPs in public databases and of high-throughput technologies able to reliably genotype a large number of SNPs in parallel at a low cost
Conclusions: regulatory SNPs (rSNPs)-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/
A direct comparison of rSNP-MAPPER against other tools for rSNP detection is difficult for at least two reasons: the lack of an up-to-date, publicly available, sufficiently large set of known rSNPs to be used as a test dataset, and the fact that, to the best of our knowledge, no other tool offers the kind of support for high-throughput rSNP analysis that is one of rSNP-MAPPER’s strengths

Summary

Introduction

Single nucleotide polymorphisms (SNPs) have become the genetic marker of choice for studies aimed at linking genotype and phenotype, thanks to the availability of a very large number of well-characterized SNPs in public databases and of high-throughput technologies able to reliably genotype a large number of SNPs in parallel at a low cost. By modifying the site’s DNA sequence, a regulatory SNP can change the its binding affinity towards the corresponding TF, which in turn can have an effect on the expression level of the downstream gene. If a method to reliably detect them is available, rSNPs could be linked with changes in gene expression levels or alternative splicing patterns, providing highly useful information for the elucidation of the corresponding regulatory networks. 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

Results

Conclusion