RNA-Seq differential expression analysis: An extended review and a software tool.
RNA-Seq differential expression analysis: An extended review and a software tool.
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DOI：
10.1371/journal.pone.0190152
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Lopes FM
中科院分区：
文献类型：
--
作者：
Costa-Silva J;Domingues D;Lopes FM
在特定条件下差异表达基因的正确识别是理解表型变异的关键。高通量转录组测序（RNA-Seq）已成为这些研究的主要选择。因此，用于从RNA-Seq数据进行差异表达分析的方法和软件的数量也迅速增加。然而，对于从RNA-Seq数据中识别差异表达基因的最合适的管道或协议没有达成共识。这项工作提出了一个扩展的审查主题，包括评估六种方法的映射读取，包括伪比对和准映射和九种方法的差异表达分析从RNA-Seq数据。基于真实的RNA-Seq数据，使用qRT-PCR数据作为参考（金标准），对所采用的方法进行评价。作为结果的一部分，我们开发了一个软件，可以执行本工作中提出的所有分析，该软件可以在https://github.com/costasilvati/consexpression上免费获得。结果表明，考虑到所采用的数据具有注释的参考基因组，作图方法对最终DEG分析的影响最小。对于所采用的实验模型，具有更一致结果的DEG识别方法是limma+voom、NOIseq和DESeq 2。此外，五种DEG识别方法之间的共识保证了DEG列表具有很高的准确性，表明不同方法的组合可以产生更合适的结果。共识选项也包括在可用的软件中使用。
The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly. However, there is no consensus about the most appropriate pipeline or protocol for identifying differentially expressed genes from RNA-Seq data. This work presents an extended review on the topic that includes the evaluation of six methods of mapping reads, including pseudo-alignment and quasi-mapping and nine methods of differential expression analysis from RNA-Seq data. The adopted methods were evaluated based on real RNA-Seq data, using qRT-PCR data as reference (gold-standard). As part of the results, we developed a software that performs all the analysis presented in this work, which is freely available at https://github.com/costasilvati/consexpression. The results indicated that mapping methods have minimal impact on the final DEGs analysis, considering that adopted data have an annotated reference genome. Regarding the adopted experimental model, the DEGs identification methods that have more consistent results were the limma+voom, NOIseq and DESeq2. Additionally, the consensus among five DEGs identification methods guarantees a list of DEGs with great accuracy, indicating that the combination of different methods can produce more suitable results. The consensus option is also included for use in the available software.
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Stolovitzky, Gustavo