Keywords: eQTLs, RNA-seq, recount2, Batch Effect, gEUVADIS
Webpages:
https://jhubiostatistics.shinyapps.io/recount/,
https://www.bioconductor.org/packages/recount recount2 is a recently launched multi-experiment resource of analysis-ready RNA-seq gene and exon count datasets for 2,041 different studies with over 70,000 human RNA-seq samples from the Sequence Read Archive (SRA), Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) projects (Collado-Torres et al. (2016)). The raw sequencing reads were processed with
Rail-RNA as described at Nellore et al. (2016). RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study can be accessed via the Bioconductor package
recount or via a
Shiny App.
We use this source of preprocessed RNA-seq expression data to present our recently developed analysis protocol for performing extensive eQTL analyses. The goal of an eQTL analysis is to detect patterns of gene expression related to specific genetic variants. We demonstrate how to integrate gene expression data from recount2 and genotype information to perform eQTL analyses and visualize the results with gene-SNP interaction plots. We explain in detail how expression and genotype data are filtered, transformed, and batch corrected. We also discuss possible pitfalls and artifacts that may occur when analyzing genomic data from different sources jointly. Our protocol is tested on a publicly available data set of the RNA-sequencing project from the GEUVADIS consortium and also applied to recently generated omics data from the GeneSTAR project at Johns Hopkins University.
References Collado-Torres, Leonardo, Abhinav Nellore, Kai Kammers, Shannon E Ellis, Margaret A Taub, Kasper D Hansen, Andrew E Jaffe, Ben Langmead, and Jeffrey Leek. 2016. “Recount: A Large-Scale Resource of Analysis-Ready RNA-seq Expression Data.”
bioRxiv. doi:
10.1101/068478.
Nellore, Abhinav, Leonardo Collado-Torres, Andrew E Jaffe, Jose Alquicira-Hernandez, Christopher Wilks, Jacob Pritt, James Morton, Jeffrey T Leek, and Ben Langmead. 2016. “Rail-RNA: Scalable Analysis of RNA-seq Splicing and Coverage.”
Bioinformatics. doi:
10.1093/bioinformatics/btw575.
