UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq.
UClncR: Ultrafast and comprehensive long non-coding RNA detection from RNA-seq.
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DOI：
10.1038/s41598-017-14595-3
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发表时间：
2017-10-27
影响因子：
4.6
通讯作者：
Kocher JP
中科院分区：
文献类型：
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作者：
Sun Z;Nair A;Chen X;Prodduturi N;Wang J;Kocher JP
长非编码RNA(Long Non-Coding RNA，LncRNA)是近年来发现的一大类具有调控功能的基因转录本。随着来自不同类型的正常和疾病组织的RNA-SEQ数据的积累，预计还会揭示更多的信息。然而，从海量的RNA-SEQ数据中发现新的LncRNAs并准确地量化已知的LncRNAs并不是一件容易的事情。在这里，我们描述了UClncR，一个超快和全面的LncRNA检测管道来应对这一挑战。UClncR获取标准的RNA-seq比对文件，执行转录本组装，预测LncRNA候选，对已知和新的LncRNA候选进行量化和注释，并生成用于下游分析的方便报告。这条管道同时容纳了无链和有链的RNA-seq，因此可以预测和量化与其他基因重叠的lncRNA。UClncR在集群环境中是完全并行的，但允许用户在没有集群的情况下按顺序运行样本。该管道可以在几分钟内处理一个典型的RNA-seq样本，并在几个小时内完成数百个样本。对来自两个测试数据集的预测LncRNA的分析表明，UClncR的准确性及其与样本临床表型的相关性。UClncR将极大地促进研究人员发现新的LncRNA，并可在http://bioinformaticstools.mayo.edu/research/UClncR.上公开获得
Long non-coding RNA (lncRNA) is a large class of gene transcripts with regulatory functions discovered in recent years. Many more are expected to be revealed with accumulation of RNA-seq data from diverse types of normal and diseased tissues. However, discovering novel lncRNAs and accurately quantifying known lncRNAs is not trivial from massive RNA-seq data. Herein we describe UClncR, an Ultrafast and Comprehensive lncRNA detection pipeline to tackle the challenge. UClncR takes standard RNA-seq alignment file, performs transcript assembly, predicts lncRNA candidates, quantifies and annotates both known and novel lncRNA candidates, and generates a convenient report for downstream analysis. The pipeline accommodates both un-stranded and stranded RNA-seq so that lncRNAs overlapping with other genes can be predicted and quantified. UClncR is fully parallelized in a cluster environment yet allows users to run samples sequentially without a cluster. The pipeline can process a typical RNA-seq sample in a matter of minutes and complete hundreds of samples in a matter of hours. Analysis of predicted lncRNAs from two test datasets demonstrated UClncR’s accuracy and their relevance to sample clinical phenotypes. UClncR would facilitate researchers’ novel lncRNA discovery significantly and is publically available at http://bioinformaticstools.mayo.edu/research/UClncR.
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影响因子：
14.9
作者：
Wang L;Park HJ;Dasari S;Wang S;Kocher JP;Li W
通讯作者：
Li W
影响因子：
46.9
作者：
Trapnell C;Williams BA;Pertea G;Mortazavi A;Kwan G;van Baren MJ;Salzberg SL;Wold BJ;Pachter L
通讯作者：
Pachter L
影响因子：
10.5
作者：
Cabili, Moran N.;Trapnell, Cole;Rinn, John L.
通讯作者：
Rinn, John L.
影响因子：
3.7
作者：
Sun K;Zhao Y;Wang H;Sun H
通讯作者：
Sun H
影响因子：
46.9
作者：
通讯作者：
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