RNA-Seq differential expression analysis: An extended review and a software tool

By Juliana Costa-Silva, D. Domingues, Fabricio M. Lopes. PLoS ONE, 2017

524 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/1b4112f5a11b41e65ecec8b457a3da0bc9bbe02b

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Citations in the corpus

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, methodology): “RNA-seq normalization plays a crucial role to ensure the validity of gene counts for downstream differential analysis (Dillies et al. 2013; Costa-Silva et al. 2017).”