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

Abstract

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.

A high-throughput segregation analysis identifies the sex chromosomes ofCannabis sativa

“…As the differential gene expression analysis methods currently available varied in performance (Schurch et al 2016;Costa-Silva et al 2017), we chose to combine several methods. Analysis contrasting the gene expression level between our 12 male and female individuals were thus performed using three R packages (i) DEseq2 version 1.10.1 (Love et al 2014), (ii) EdgeR version 3.26.9 (Robinson et al 2010) both relying on negative binomial distribution of read count modelling and (iii) limma-voom version 3.26.9 (Ritchie et al 2015) based on log-normal distribution modelling to take into account the sampling variance of small read counts.…”

Effects of technical noise on bulk RNA-seq differential gene expression inference

“…While most studies comparing RNA-seq analysis pipelines have focused on benchmarking the normalisation and statistical testing of DE (6,21,28,29), we elected instead to focus on the joint effects sequencing noise and of analytical noise derived from alignment and count estimation methods on DE calls. A previous study evaluating the impact of alignment methods on the DE analysis, without taking into account the presence or extent of technical noise, explored these methods in combination with a single read quantification tool (30). We have shown that a vast proportion of variability in the final output introduced by computational choices is attributable to the read quantification tool.…”

Interpretation of ‘Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes

“…We conducted the simulations against these methods to illustrate the need for new approaches to study single-subject transcripts in TCWR conditions. In addition, a true gold standard to evaluate iDEG and other methods is not as simple as obtaining replicates and running conventional methods as pointed out by recent papers 34,35 .…”