MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.
MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.
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DOI：
10.1093/bioinformatics/bty208
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发表时间：
2018-09-15
期刊：
影响因子：
--
通讯作者：
Backofen R
中科院分区：
文献类型：
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作者：
Gawronski AR;Uhl M;Zhang Y;Lin YY;Niknafs YS;Ramnarine VR;Malik R;Feng F;Chinnaiyan AM;Collins CC;Sahinalp SC;Backofen R
长链非编码rna （lncrna）被定义为长度超过200nt的转录本，不会被翻译成蛋白质。这些转录本通常经过加工（剪接、盖帽和聚腺苷化），其中一些已知具有重要的生物学功能。然而，大多数lncrna的功能是未知的或知之甚少的。然而，由于lncrna在癌症中的潜在作用，它们受到了广泛的关注，并且比以往任何时候都更需要计算工具来预测它们可能的作用机制。从根本上说，大多数已知的lncRNA机制涉及RNA-RNA和/或rna -蛋白相互作用。通过对每种相互作用的准确预测和这些预测的整合，有可能阐明给定lncRNA的潜在机制。在这里，我们介绍MechRNA，一个用于证实RNA-RNA相互作用预测和蛋白质结合预测的管道，用于识别涉及特定靶点或转录组范围内可能的lncRNA机制。第一阶段使用具有额外功能的IntaRNA2版本，用于有效预测具有很长输入序列的RNA-RNA相互作用，允许大规模分析lncRNA相互作用，并且几乎没有损失最优性。第二阶段整合由GraphProt预先计算的lncRNA和靶标的蛋白质结合信息。最后阶段涉及推断每个lncRNA/靶对最可能的机制。这是通过从预测的相互作用、这些相互作用的相对位置和相关数据中生成候选机制，然后使用组合p值选择最可能的机制解释来实现的。我们将MechRNA应用于许多最近发现的与癌症相关的lncrna （PCAT1、PCAT29和ARLnc1）以及两个研究得很好的lncrna （PCA3和7SL）。这导致鉴定出每个lncRNA的数百个高置信度潜在靶点和相应的机制。这些预测包括已知的7SL与HuR结合肿瘤抑制因子TP53的竞争机制，以及扩展PCAT1和ARLn1及其靶点BRCA2和AR的已知机制。对于PCAT1-BRCA2，其机制涉及与HuR的竞争性结合，我们使用HuR免疫沉淀试验证实了这一点。MechRNA可在https://bitbucket.org/compbio/mechrna下载。补充数据可在生物信息学网站获得。
Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA–RNA and/or RNA–protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Here, we introduce MechRNA, a pipeline for corroborating RNA–RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA–RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary data are available at Bioinformatics online.
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