CoRAL: predicting non-coding RNAs from small RNA-sequencing data.
CoRAL: predicting non-coding RNAs from small RNA-sequencing data.
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DOI：
10.1093/nar/gkt426
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发表时间：
2013-08
影响因子：
14.9
通讯作者：
Wang LS
中科院分区：
文献类型：
--
作者：
Leung YY;Ryvkin P;Ungar LH;Gregory BD;Wang LS
几乎整个人类基因组都是转录的，这一令人惊讶的观察结果意味着，除了它们惊人的多样性外，我们对许多新兴rna类别的功能知之甚少。传统的RNA功能预测方法依赖于序列或比对信息，这些信息在对各种非编码RNA （ncRNAs）集合进行分类的能力上受到限制。为了解决这个问题，我们开发了基于长度分析的RNA分类（CoRAL），这是一种基于机器学习的RNA分子分类方法。CoRAL使用生物学上可解释的特征，包括片段长度和切割特异性来区分不同的ncRNA群体。我们使用来自四种人类组织类型的全基因组小RNA测序数据集对CoRAL进行了评估，并能够对六种不同类型的RNA进行分类，交叉验证准确率为80%。CoRAL的分析显示，在所有评估的人类组织类型中，微小rna、小核核rna和转座子来源的rna具有高度可辨别性和一致性，而长基因间ncrna、小细胞质rna和小核rna的模式不太一致。在组织类型中可靠地注释位点的能力证明了CoRAL在特征不太明确的生物体中使用小RNA测序数据表征ncRNAs的潜力。
The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.
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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
影响因子：
3
作者：
Statnikov, Alexander;Wang, Lily;Aliferis, Constantin F.
通讯作者：
Aliferis, Constantin F.
DOI：
10.1093/bioinformatics/btq363
发表时间：
2010-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Erhard F;Zimmer R
通讯作者：
Zimmer R
影响因子：
64.5
作者：
BLACK, DL;CHABOT, B;STEITZ, JA
通讯作者：
STEITZ, JA
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P