Differentially Expressed Genes

DElite is a novel R package for DE analysis. With a single command line, DE returns the output of edgeR, limma, dearseq and DESeq2, both as individual results and as a combined output. DElite offers user-friendly functionality, accompanied by a detailed report, and allows advanced users to customize their analyses to a high degree.

This project is a comparison between two development stages in berry (Véraison and mature) and typical abiotic stress treatments (ABA-treated and Polyol-treated). After RNA-sequencing (TruSeq Stranded mRNA cDNA libraries, ~ 600 ng of berry total RNA) about 70 million paired-end reads of 50 bp were produced per sample. Principal component and pairwise comparisons based on ANOVA-like approach of edgeR were used to identify the differentially expressed genes (DEG) in these four groups of samples.

The last version of Vitis vinifera reference geneme was used for the alignment (https://integrape.eu/resources/genes-genomes/genome-accessions/).

NOTE// SIMPLE DEG ANALYSIS): In order to perform a global analysis of expression, not subject to sequence analysis 50 bp paired end reads is better than 100 bp single end reads. Why? 50 base-pair paired-end reads span a longer region of the transcript. Each read represents one end of a ~200-300 base-pair RNA fragment, compared to a 100 base-pair read which only gives you information about 100 bases. A larger fragment means you are more likely to span a splice junction, insertion, or deletion. Therefore 50 bp is preferable. Remember that with next gen sequencing technology, for the most part your read is only a "tag" that tells you where in the genome the fragment originated. As long as the "tag" is long enough to be unique (and 50 bp is for the most part) you are set.

Course of the subject "Analysis of transcriptomic data" whose syllabus I developed and taught in 2022 for the International University of Valencia (made in Markdown).

DEGs analysis of bulk RNA-seq data using edgeR