Sebnif: an integrated bioinformatics pipeline for the identification of novel large intergenic noncoding RNAs (lincRNAs)--application in human skeletal muscle cells.
Sebnif: an integrated bioinformatics pipeline for the identification of novel large intergenic noncoding RNAs (lincRNAs)--application in human skeletal muscle cells.
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DOI：
10.1371/journal.pone.0084500
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Sun H
中科院分区：
文献类型：
--
作者：
Sun K;Zhao Y;Wang H;Sun H
转录组测序数据的从头组装已被广泛用于鉴定大的基因间非编码RNA（lincRNA），这是一类涉及许多生物过程的新型基因调控因子。为了将真实的lincRNA转录物与成千上万的组装产物区分开，需要应用一系列过滤步骤，例如转录物长度、表达水平和编码潜力的过滤器。然而，一个易于使用和公开可用的生物信息学管道，集成这些过滤器还没有。因此，我们实施了sebnif，这是一个整合的生物信息学管道，以促进发现真正的新型lincRNA，这些lincRNA适合于进一步的功能表征。具体来说，sebnif是唯一一个实现识别高质量单外显子lincRNA算法的管道，这些lincRNA在许多研究中经常被忽略。为了证明sebnif的使用，我们将其应用于来自人类骨骼肌细胞（HSkMC）的真实的生物RNA-seq数据集，并构建了包含917个高度可靠的lincRNA的新型lincRNA目录。Sebnif可在http://sunlab.lihs.cuhk.edu.hk/sebnif/上获得。
Ab initio assembly of transcriptome sequencing data has been widely used to identify large intergenic non-coding RNAs (lincRNAs), a novel class of gene regulators involved in many biological processes. To differentiate real lincRNA transcripts from thousands of assembly artifacts, a series of filtering steps such as filters of transcript length, expression level and coding potential, need to be applied. However, an easy-to-use and publicly available bioinformatics pipeline that integrates these filters is not yet available. Hence, we implemented sebnif, an integrative bioinformatics pipeline to facilitate the discovery of bona fide novel lincRNAs that are suitable for further functional characterization. Specifically, sebnif is the only pipeline that implements an algorithm for identifying high-quality single-exonic lincRNAs that were often omitted in many studies. To demonstrate the usage of sebnif, we applied it on a real biological RNA-seq dataset from Human Skeletal Muscle Cells (HSkMC) and built a novel lincRNA catalog containing 917 highly reliable lincRNAs. Sebnif is available at http://sunlab.lihs.cuhk.edu.hk/sebnif/.
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影响因子：
3
作者：
Sun L;Zhang Z;Bailey TL;Perkins AC;Tallack MR;Xu Z;Liu H
通讯作者：
Liu H
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
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Pachter L
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通讯作者：
Hubbard TJ
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7
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通讯作者：
Haussler, D