Two-group comparison of differential expression (DE) is the most common analysis of transcriptome data. For RNA-seq data, the comparison is usually performed on a gene-level matrix of read counts, with the read counts corresponding to the number of sequencing reads mapped to each gene in each RNA-seq sample.
For RNA-seq data the raw sequencing reads need to be aligned to the reference genome and transcriptome, using any alignment program. Next, the aligned reads should be assigned to annotated genes or transcripts to generate a read count matrix. RNA-seq 2G accepts other types of data, such as those generated by the proteomics and Nanostring technologies, as long as the raw data was processed similarly to generate a integer matrix.
RNAseq 2G provides a user-friendly web portal to run a DE analysis using any of the available methods. Each analysis will be assigned a random ID and its results can be re-visited by specifying the ID. To perform an analysis, go to
rnaseq2g.awsomics.org and set it up with the following 3 steps.
An alternative to use RNA-seq 2G is to directly call the DeRNAseq {DEGandMore} function within R. This option is more suitable for DE analysis runs using any of the slow methods (see Table 1). When any slow DE methods are selected, the online waiting might be too long and users should run the DE analysis offline, but can later upload their results to RNAseq 2G for visualization. Running the offline analysis takes some basic R skills and a few simple steps.