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mrSNP: Software to detect SNP effects on microRNA binding

Mehmet Deveci1*, Ümit V Çatalyürek2 and Amanda Ewart Toland3

Author Affiliations

1 Biomedical Informatics, Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA

2 Biomedical Informatics, Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA

3 Molecular Virology, Immunology and Medical Genetics, The Ohio State University, Columbus, Ohio, USA

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BMC Bioinformatics 2014, 15:73  doi:10.1186/1471-2105-15-73

Published: 15 March 2014



MicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3’untranslated regions (3’UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or blocked translation resulting in decreased expression of the targeted gene. Single nucleotide polymorphisms (SNPs) have been found in 3’UTRs that disrupt normal miRNA binding or introduce new binding sites and some of these have been associated with disease pathogenesis. This raises the importance of detecting miRNA targets and predicting the possible effects of SNPs on binding sites. In the last decade a number of studies have been conducted to predict the location of miRNA binding sites. However, there have been fewer algorithms published to analyze the effects of SNPs on miRNA binding. Moreover, the existing software has some shortcomings including the requirement for significant manual labor when working with huge lists of SNPs and that algorithms work only for SNPs present in databases such as dbSNP. These limitations become problematic as next-generation sequencing is leading to large numbers of novel variants in 3’UTRs.


In order to overcome these issues, we developed a web-server named mrSNP which predicts the impact of a SNP in a 3’UTR on miRNA binding. The proposed tool reduces the manual labor requirements and allows users to input any SNP that has been identified by any SNP-calling program. In testing the performance of mrSNP on SNPs experimentally validated to affect miRNA binding, mrSNP correctly identified 69% (11/16) of the SNPs disrupting binding.


mrSNP is a highly adaptable and performing tool for predicting the effect a 3’UTR SNP will have on miRNA binding. This tool has advantages over existing algorithms because it can assess the effect of novel SNPs on miRNA binding without requiring significant hands on time.

miRNA; SNP; mRNA; microRNA binding