Abstract
BackgroundRNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis.ResultsDiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress.ConclusionsDiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results.
Highlights
RNAseq is nowadays the method of choice for transcriptome analysis
We illustrate the use of DiCoExpress by analysing a dataset associated with the publication of Haddad et al [42]
Checking the quality control results in Brassica_napus_Data_Quality_Control. pdf output file, we observe a higher number of reads in the mature leaf samples compared to the root samples; Table 1 Target table of Brassica napus dataset in R
Summary
RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high num‐ ber of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. Multiple methods, based on different statistical modelling of data, are available to perform differential expression analysis. The linear models and their generalized extensions for negative binomial distributions (GLM) have been proposed to account for the versatility of multifactorial experiments. They are available in the R-package limma [12] for the linear models and in the R-packages edgeR [10] and DESeq2 [11] for the generalized linear models. Pearson’s or Spearman’s correlations, WGCNA (Weighted correlation network analysis) method [13], hierarchical clustering and K-means are the most conventional approaches found in the literature [14, 15]. A model selection criterion allows determining the most appropriate cluster number [16, 17]
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