Biomedical Informatics, Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
BMC Bioinformatics. 2014 Mar 15;15:73. doi: 10.1186/1471-2105-15-73.
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.
MicroRNAs (miRNAs) 是短链(19-23 个核苷酸)的非编码 RNA,可与靶向信使 RNA (mRNA) 的 3'非翻译区 (3'UTR) 结合。结合会导致转录本降解或翻译受阻,从而导致靶基因表达降低。在 3'UTR 中发现了单核苷酸多态性 (SNP),这些 SNP 会破坏正常的 miRNA 结合或引入新的结合位点,其中一些与疾病发病机制有关。这就提高了检测 miRNA 靶标和预测 SNP 对结合位点可能产生的影响的重要性。在过去的十年中,已经进行了许多研究来预测 miRNA 结合位点的位置。然而,发表的用于分析 SNP 对 miRNA 结合影响的算法较少。此外,现有的软件存在一些缺点,包括在处理大量 SNP 列表时需要大量人工劳动,并且算法仅适用于如 dbSNP 等数据库中存在的 SNP。随着下一代测序导致 3'UTR 中出现大量新变体,这些限制变得成问题。
为了克服这些问题,我们开发了一个名为 mrSNP 的网络服务器,用于预测 3'UTR 中 SNP 对 miRNA 结合的影响。该工具减少了人工劳动需求,并允许用户输入任何 SNP,这些 SNP 已经由任何 SNP 调用程序确定。在测试 mrSNP 对实验验证影响 miRNA 结合的 SNP 的性能时,mrSNP 正确识别了 69%(11/16)破坏结合的 SNP。
mrSNP 是一种高度适应和性能良好的工具,用于预测 3'UTR SNP 对 miRNA 结合的影响。与现有算法相比,该工具具有优势,因为它可以在不大量人工干预的情况下评估新型 SNP 对 miRNA 结合的影响。