Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq
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- 作者：Claire R. Williams ; Alyssa Baccarella ; Jay Z. Parrish ; Charles C. Kim
- 关键词：Monocytes ; Classical ; Nonclassical ; RNA ; Seq ; Gene expression analysis
- 刊名：BMC Bioinformatics
- 出版年：2017
- 出版时间：December 2017
- 年：2017
- 卷：18
- 期：1
- 全文大小：1841KB
- 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Algorithms;
- 出版者：BioMed Central
- ISSN：1471-2105
- 卷排序：18
文摘
BackgroundRNA-Seq has supplanted microarrays as the preferred method of transcriptome-wide identification of differentially expressed genes. However, RNA-Seq analysis is still rapidly evolving, with a large number of tools available for each of the three major processing steps: read alignment, expression modeling, and identification of differentially expressed genes. Although some studies have benchmarked these tools against gold standard gene expression sets, few have evaluated their performance in concert with one another. Additionally, there is a general lack of testing of such tools on real-world, physiologically relevant datasets, which often possess qualities not reflected in tightly controlled reference RNA samples or synthetic datasets.