Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench.
Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench.
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DOI：
10.1261/rna.059360.116
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Moulton V
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文献类型：
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作者：
Beckers M;Mohorianu I;Stocks M;Applegate C;Dalmay T;Moulton V
最近，高通量测序（HTS）揭示了关于真核生物中小RNA（sRNA）群体的令人信服的细节。这些20至25 nt的非编码RNA可以通过作为称为RNA沉默的序列特异性调节机制的指导来影响基因表达。测序深度和每个项目样本数量的增加，通过促进表达模式的研究，使人们能够更好地理解sRNA的作用。然而，生物学假设的复杂性加上缺乏适当的工具，往往导致现有数据的挖掘不足，从而导致对所涉及的生物学机制的描述不完整。为了全面研究sRNA数据集中的差异表达，我们提出了一个新的交互式管道，指导研究人员完成数据预处理和分析的各个阶段。这包括各种工具，其中一些是我们专门为sRNA分析开发的，用于sRNA样品的质量检查和标准化，以及用于检测差异表达的sRNA和鉴定所得表达模式的工具。该管道可在UEA sRNA数据库中使用，这是一个用户友好的软件包，用于处理sRNA数据集。我们演示了使用的H上的管道。sapiens数据集; a B上的其他示例。terrestris数据集和A. thaliana数据集在补充信息中描述。还包括与现有方法的比较，其中举例说明了sRNA分析需要解决的一些问题以及如何使用新管道来实现这一目标。
Recently, high-throughput sequencing (HTS) has revealed compelling details about the small RNA (sRNA) population in eukaryotes. These 20 to 25 nt noncoding RNAs can influence gene expression by acting as guides for the sequence-specific regulatory mechanism known as RNA silencing. The increase in sequencing depth and number of samples per project enables a better understanding of the role sRNAs play by facilitating the study of expression patterns. However, the intricacy of the biological hypotheses coupled with a lack of appropriate tools often leads to inadequate mining of the available data and thus, an incomplete description of the biological mechanisms involved. To enable a comprehensive study of differential expression in sRNA data sets, we present a new interactive pipeline that guides researchers through the various stages of data preprocessing and analysis. This includes various tools, some of which we specifically developed for sRNA analysis, for quality checking and normalization of sRNA samples as well as tools for the detection of differentially expressed sRNAs and identification of the resulting expression patterns. The pipeline is available within the UEA sRNA Workbench, a user-friendly software package for the processing of sRNA data sets. We demonstrate the use of the pipeline on a H. sapiens data set; additional examples on a B. terrestris data set and on an A. thaliana data set are described in the Supplemental Information. A comparison with existing approaches is also included, which exemplifies some of the issues that need to be addressed for sRNA analysis and how the new pipeline may be used to do this.