SubmiRine: assessing variants in microRNA targets using clinical genomic data sets.
SubmiRine: assessing variants in microRNA targets using clinical genomic data sets.
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DOI：
10.1093/nar/gkv256
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发表时间：
2015-04-30
影响因子：
14.9
通讯作者：
Baxevanis AD
中科院分区：
文献类型：
--
作者：
Maxwell EK;Campbell JD;Spira A;Baxevanis AD
MicroRNA (miRNA) 通过与靶 mRNA 转录物上的部分互补序列结合来调节基因表达，从而导致其降解、去腺苷化或抑制其翻译。基因组变异可以通过修饰 miRNA 靶位点来改变 miRNA 调控，多种人类疾病表型已与此类 miRNA 靶位点变异 (miR-TSV) 相关。然而，由于假阳性率较高，对功能性 miR-TSV 进行系统性全基因组鉴定很困难；功能性 miRNA 识别序列可短至 6 个核苷酸，而人类基因组编码数千个 miRNA。此外，虽然大规模临床基因组数据集变得越来越普遍，但现有的 miR-TSV 预测方法并非旨在分析这些数据。在这里，我们提出了一种名为 SubmiRine 的开源工具，旨在对新的临床基因组数据集中发现的变异系统地执行有效的 miR-TSV 预测。最重要的是，SubmiRine 允许根据预测的 miR-TSV 发挥功能的相对概率对它们进行优先级排序。我们使用来自慢性阻塞性肺疾病 (COPD) 大规模队列研究的整合临床基因组数据展示了 SubmiRine 的结果，做出了许多高分、新颖的 miR-TSV 预测。我们还证明了 SubmiRine 预测已知 miR-TSV 并对其进行优先排序的能力，这些 miR-TSV 已在之前的研究中经过实验验证。
MicroRNAs (miRNAs) regulate gene expression by binding to partially complementary sequences on target mRNA transcripts, thereby causing their degradation, deadenylation, or inhibiting their translation. Genomic variants can alter miRNA regulation by modifying miRNA target sites, and multiple human disease phenotypes have been linked to such miRNA target site variants (miR-TSVs). However, systematic genome-wide identification of functional miR-TSVs is difficult due to high false positive rates; functional miRNA recognition sequences can be as short as six nucleotides, with the human genome encoding thousands of miRNAs. Furthermore, while large-scale clinical genomic data sets are becoming increasingly commonplace, existing miR-TSV prediction methods are not designed to analyze these data. Here, we present an open-source tool called SubmiRine that is designed to perform efficient miR-TSV prediction systematically on variants identified in novel clinical genomic data sets. Most importantly, SubmiRine allows for the prioritization of predicted miR-TSVs according to their relative probability of being functional. We present the results of SubmiRine using integrated clinical genomic data from a large-scale cohort study on chronic obstructive pulmonary disease (COPD), making a number of high-scoring, novel miR-TSV predictions. We also demonstrate SubmiRine's ability to predict and prioritize known miR-TSVs that have undergone experimental validation in previous studies.
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影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者：
Tuschl T
影响因子：
3.9
作者：
Barenboim, Maxim;Zoltick, Brad J.;Guo, Yongjian;Weinberger, Daniel R.
通讯作者：
Weinberger, Daniel R.
影响因子：
4.4
作者：
Bruno AE;Li L;Kalabus JL;Pan Y;Yu A;Hu Z
通讯作者：
Hu Z
DOI：
10.1073/pnas.0802682105
发表时间：
2008-05-20
影响因子：
11.1
作者：
Jazdzewski, Krystian;Murray, Elizabeth L.;de la Chapelle, Albert
通讯作者：
de la Chapelle, Albert
影响因子：
4.4
作者：
Liu C;Zhang F;Li T;Lu M;Wang L;Yue W;Zhang D
通讯作者：
Zhang D