Project for the Transcriptomic course of "Bioinformatics for Computational Genomics" MSc.
The vignette can be visualized here.
In this work RNA-seq data are analyzed in order to find and characterize differentially expressed (DE) genes. RNA-seq experiments are in fact widely used to understand how RNA-based mechanisms impact gene regulation, and thus disease and phenotypic variation as in tumoral contexts.
The RNA-seq data were retrieved from Recount2 as part of the GTEx project database. Three tissues (i.e., liver, heart and colon) with three replicates per tissue were downloaded and provided in normalized/scaled "Ranged Summarized Experiment" format of Recount.
EdgeR was choosen for the analysis of the gene expression data.
The DE genes call was done performing all the pairwise comparison:
- Colon vs heart
- Colon vs liver
- Heart vs liver
Starting from the list of DE genes for each tissue we retrieved:
- genes found to be up-(down-)regulated with respect to either one of the other two
- genes found to be up- (down-) regulated with respect to both the other two
As a final step, EnrichR, a common functional enrichment analysis tool, was used to determine whether the enriched categories, GO annotations, and pathways were consistent with the up/down regulated genes of the considered tissues.
Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology, 2017. doi: 10.1038/nbt.3838.
Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140.
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14).