RSeQC: quality control of RNA-seq experiments
RSeQC: quality control of RNA-seq experiments
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DOI：
10.1093/bioinformatics/bts356
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发表时间：
2012-08-15
期刊：
影响因子：
5.8
通讯作者：
Li, Wei
中科院分区：
文献类型：
--
作者：
Wang, Liguo;Wang, Shengqin;Li, Wei
动机：RNA-seq已被广泛用于转录组研究。质量控制（QC）对于确保RNA-seq数据具有高质量并适用于后续分析至关重要。然而，由于RNA-seq数据的庞大规模和多功能性，QC是一项耗时且复杂的任务。因此，一个方便和全面的QC工具来评估RNA-seq quality is seriously needed.Results：我们开发了RSeQC包，全面评估RNA-seq实验的不同方面，如序列质量，GC偏倚，聚合酶链反应偏倚，核苷酸组成偏倚，测序深度，链特异性，覆盖均匀性和基因组结构上的读段分布。RSeQC将SAM和BAM文件作为输入，这些文件可以由大多数RNA-seq映射工具以及广泛用于基因模型的BED文件生成。RSeQC中的大多数模块利用R脚本进行可视化，并且它们在处理包含数亿比对的大型BAM/SAM文件时非常有效。
Motivation: RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed.Results: We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments.