Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis.
Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis.
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DOI：
10.1371/journal.pone.0031630
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Calogero RA
中科院分区：
文献类型：
--
作者：
Cordero F;Beccuti M;Arigoni M;Donatelli S;Calogero RA
大规模并行测序方法(MPS)可以扩展和改进通过传统微阵列技术获得的知识，无论是针对mRNAs还是针对短的非编码RNA，例如miRNAs。用于提取和解释信息的处理方法是处理从短读取序列产生的海量数据的一个重要方面。尽管用于MPS数据分析的计算工具的数量不断增加，但它们作为复杂分析管道的一部分的优势和劣势尚未得到很好的调查。一个基准的MPS miRNA数据集，类似于在生物复制实验中miRNAs被添加的情况，通过将公开可用的MPS尖峰miRNAs数据集与来自健康捐赠者外周血单核细胞的MPS数据合并来组装。使用这个数据集，我们观察到，如果以整个基因组为参考，在miRNAs重复的情况下，短读数估计被严重低估。此外，miRNAs检测的灵敏度强烈依赖于分析中使用的主要工具。在专门用于miRNA检测的六种比对试剂中，虾和MicroRazerS显示出最高的灵敏度。差分表达式估计是一种非常有效的方法。在所研究的五种工具中，其中两种(DESseq，baySeq)在检测差异表达方面表现出很好的特异性和敏感性。我们的分析提供的结果允许为miRNAs数字定量分析定义一个明确和简单的分析优化工作流程。
Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis.
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