A non-biased framework for the annotation and classification of the non-miRNA small RNA transcriptome
A non-biased framework for the annotation and classification of the non-miRNA small RNA transcriptome
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DOI：
10.1093/bioinformatics/btr527
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发表时间：
2011-11-15
期刊：
影响因子：
5.8
通讯作者：
Marti, Eulalia
中科院分区：
文献类型：
--
作者：
Pantano, Lorena;Estivill, Xavier;Marti, Eulalia
动机：高通量测序技术的最新进展在很大程度上有助于揭示小非编码RNA （sRNAs）的高度复杂景观，包括从长非编码RNA衍生的新型非规范sRNAs，重复元件，转录起始位点和剪接位点区域等。已发表的sRNA数据分析框架侧重于miRNA检测和预测，忽略了数据集中的进一步信息。因此，目前缺乏鉴定和分类不属于miRNA家族的srna的工具。结果：在这里，我们提出了SeqCluster，这是目前可用的SeqBuster工具的扩展，用于在不同水平上识别和分析未被注释或预测为mirna的sRNAs。这个新的模块处理序列映射到多个位置，并允许与数据进行高度通用和用户友好的交互，以便轻松分类具有假定功能重要性的sRNA序列。使用SeqCluster和不同的sRNA数据集，我们能够检测到迄今为止描述的所有已知类别的sRNA。
Motivation: Recent progress in high-throughput sequencing technologies has largely contributed to reveal a highly complex landscape of small non-coding RNAs (sRNAs), including novel non-canonical sRNAs derived from long non-coding RNA, repeated elements, transcription start sites and splicing site regions among others. The published frameworks for sRNA data analysis are focused on miRNA detection and prediction, ignoring further information in the dataset. As a consequence, tools for the identification and classification of the sRNAs not belonging to miRNA family are currently lacking.Results: Here, we present, SeqCluster, an extension of the currently available SeqBuster tool to identify and analyze at different levels the sRNAs not annotated or predicted as miRNAs. This new module deals with sequences mapping onto multiple locations and permits a highly versatile and user-friendly interaction with the data in order to easily classify sRNA sequences with a putative functional importance. We were able to detect all known classes of sRNAs described to date using SeqCluster with different sRNA datasets.