Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs.
Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs.
复制标题
DOI：
10.1016/j.ymeth.2013.10.002
复制
发表时间：
2014-05-01
期刊：
影响因子：
4.8
通讯作者：
Wang, Li-San
中科院分区：
文献类型：
--
作者：
Ryvkin, Paul;Leung, Yuk Yee;Ungar, Lyle H.;Gregory, Brian D.;Wang, Li-San
高通量测序的最新进展使研究人员能够比以往更详细地检查转录组。使用一种被称为高通量小rna测序的方法，我们现在可以以极高的灵敏度分析小调控rna（如microRNAs和小干扰rna (sirna)）的表达。然而，细胞中存在许多其他类型的小rna (<50 nt)，包括来自snoRNAs（小核仁rna）， snrna（小核rna）， scRNAs（小细胞质rna）， tRNAs（转移rna）和转座子来源的rna的片段。在这里，我们提出了一个用户指南的珊瑚（分类RNA的长度分析），一个计算方法来区分不同类别的RNA使用高通量小RNA测序数据。CoRAL不仅可以高精度地区分RNA类别，而且还可以使用与小RNA生物发生途径相关的特征。通过这样做，CoRAL可以让生物学家了解不同RNA加工途径的特征，以及这些途径在组织类型、生物条件甚至不同物种之间的差异。CoRAL可在http://wanglab.pcbi.upenn.edu/coral/上获得。
Recent advances in high-throughput sequencing allow researchers to examine the transcriptome in more detail than ever before. Using a method known as high-throughput small RNA-sequencing, we can now profile the expression of small regulatory RNAs such as microRNAs and small interfering RNAs (siRNAs) with a great deal of sensitivity. However, there are many other types of small RNAs (<50 nt) present in the cell, including fragments derived from snoRNAs (small nucleolar RNAs), snRNAs (small nuclear RNAs), scRNAs (small cytoplasmic RNAs), tRNAs (transfer RNAs), and transposon-derived RNAs. Here, we present a user’s guide for CoRAL (Classification of RNAs by Analysis of Length), a computational method for discriminating between different classes of RNA using high-throughput small RNA-sequencing data. Not only can CoRAL distinguish between RNA classes with high accuracy, but it also uses features that are relevant to small RNA biogenesis pathways. By doing so, CoRAL can give biologists a glimpse into the characteristics of different RNA processing pathways and how these might differ between tissue types, biological conditions, or even different species. CoRAL is available at http://wanglab.pcbi.upenn.edu/coral/.
登录
查看更多内容
影响因子：
14.9
作者：
Ebhardt HA;Tsang HH;Dai DC;Liu Y;Bostan B;Fahlman RP
通讯作者：
Fahlman RP
类似于盒子C/d snornas的人类miRNA前体的识别。
影响因子：
14.9
作者：
Ono M;Scott MS;Yamada K;Avolio F;Barton GJ;Lamond AI
通讯作者：
Lamond AI
影响因子：
14.9
作者：
Leung YY;Ryvkin P;Ungar LH;Gregory BD;Wang LS
通讯作者：
Wang LS
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL