Skip to main content

Research Repository

Advanced Search

Endothelial Cell RNA-Seq Data: Differential Expression and Functional Enrichment Analyses to Study Phenotypic Switching

Pinel, Guillermo Díez; Horder, Joseph L.; King, John R.; McIntyre, Alan; Mongan, Nigel P.; López, Gonzalo Gómez; Benest, Andrew V.

Authors

Guillermo Díez Pinel

Joseph L. Horder

JOHN KING JOHN.KING@NOTTINGHAM.AC.UK
Professor of Theoretical Mechanics

NIGEL MONGAN nigel.mongan@nottingham.ac.uk
Professor of Oncology

Gonzalo Gómez López



Contributors

Andrew V. Benest
Editor

Abstract

RNA-seq is a common approach used to explore gene expression data between experimental conditions or cell types and ultimately leads to information that can shed light on the biological processes involved and inform further hypotheses. While the protocols required to generate samples for sequencing can be performed in most research facilities, the resulting computational analysis is often an area in which researchers have little experience. Here we present a user-friendly bioinformatics workflow which describes the methods required to take raw data produced by RNA sequencing to interpretable results. Widely used and well documented tools are applied. Data quality assessment and read trimming were performed by FastQC and Cutadapt, respectively. Following this, STAR was utilized to map the trimmed reads to a reference genome and the alignment was analyzed by Qualimap. The subsequent mapped reads were quantified by featureCounts. DESeq2 was used to normalize and perform differential expression analysis on the quantified reads, identifying differentially expressed genes and preparing the data for functional enrichment analysis. Gene set enrichment analysis identified enriched gene sets from the normalized count data and clusterProfiler was used to perform functional enrichment against the GO, KEGG, and Reactome databases. Example figures of the functional enrichment analysis results were also generated. The example data used in the workflow are derived from HUVECs, an in vitro model used in the study of endothelial cells, published and publicly available for download from the European Nucleotide Archive.

Online Publication Date Jan 31, 2022
Publication Date 2022
Deposit Date Apr 9, 2023
Pages 369-426
Series Title Methods in Molecular Biology
Series Number 2441
Book Title Angiogenesis: Methods and Protocol
ISBN 9781071620588
DOI https://doi.org/10.1007/978-1-0716-2059-5_29
Public URL https://nottingham-repository.worktribe.com/output/7409721
Publisher URL https://link.springer.com/protocol/10.1007/978-1-0716-2059-5_29