Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.
Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.
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DOI：
10.1093/nar/gkx836
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发表时间：
2017-12-01
影响因子：
14.9
通讯作者：
Enright AJ
中科院分区：
文献类型：
--
作者：
Vitsios DM;Kentepozidou E;Quintais L;Benito-Gutiérrez E;van Dongen S;Davis MP;Enright AJ
MicroRNAs(MiRNAs)的发现仍然是一个重要的问题，特别是考虑到高通量测序、细胞分选和单细胞生物学的发展。虽然大量的miRNAs已经被注释，但很可能有大量的miRNAs在非常特定的细胞类型中表达，并且仍然难以捉摸。测序使我们能够快速准确地从小RNA-Seq数据中识别已知的miRNAs的表达。MiRNAs的生物发生导致在其序列中观察到非常特殊的特征。简而言之，miRNAs通常有一个定义明确的5‘端和一个更灵活的3’端，并有可能发生3‘端的拖尾事件，如尿苷化。以往预测新miRNAs的方法通常涉及分析从基因组序列获得的miRNA前体发夹序列的结构特征。我们推测，有可能通过使用这些直接从测序阅读中观察到的生物发生特征，单独或除了来自基因组数据的结构分析，来识别miRNAs。为此，我们开发了Mirnovo，一种基于机器学习的算法，它能够直接从小RNA-Seq数据中识别动植物中已知和新的miRNAs，无论是否有参考基因组。此方法的性能与现有工具相当，但使用起来更简单，运行时间更短。它的性能和准确性已经在多个数据集上进行了测试，包括基因组组装不佳的物种、RNaseIII(DROSHA和/或DICER)缺陷样本和单细胞(胚胎和成体阶段)。
The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large numbers of miRNAs that are expressed in very particular cell types and remain elusive. Sequencing allows us to quickly and accurately identify the expression of known miRNAs from small RNA-Seq data. The biogenesis of miRNAs leads to very specific characteristics observed in their sequences. In brief, miRNAs usually have a well-defined 5′ end and a more flexible 3′ end with the possibility of 3′ tailing events, such as uridylation. Previous approaches to the prediction of novel miRNAs usually involve the analysis of structural features of miRNA precursor hairpin sequences obtained from genome sequence. We surmised that it may be possible to identify miRNAs by using these biogenesis features observed directly from sequenced reads, solely or in addition to structural analysis from genome data. To this end, we have developed mirnovo, a machine learning based algorithm, which is able to identify known and novel miRNAs in animals and plants directly from small RNA-Seq data, with or without a reference genome. This method performs comparably to existing tools, however is simpler to use with reduced run time. Its performance and accuracy has been tested on multiple datasets, including species with poorly assembled genomes, RNaseIII (Drosha and/or Dicer) deficient samples and single cells (at both embryonic and adult stage).
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影响因子：
64.8
作者：
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影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
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影响因子：
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DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
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通讯作者：
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影响因子：
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通讯作者：
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