RNA SEQUENCING DATA ANALYSIS

RNA sequencing data analysis brings to light the intricate mechanisms of gene regulation.

TRANSCRIPTOME-WIDE ANALYSES of gene expression are extremely popular among researchers studying gene regulation in biological systems ranging from single cells to tissues and complex microbiomes. RNA-seq data allows for a wide range of analyses to address countless research questions across the fields of biology and biomedicine.

Below we present some of the most common analyses we perform on RNA-seq data. The explorative, differential expression and pathway analyses largely apply to other high-throughput expression data as well, such as expression microarray or proteomic data.

We hope that the examples below inspire you to appreciate just how rich the world of RNA-sequencing is. If you are planning an RNA-seq experiment and wish to learn how we can help you to get the most out of your data, leave us a message and we will book you a short call with our expert.

Exploratory gene expression analysis

• DIFFERENTIAL EXPRESSION ANALYSIS is a statistical comparison of two sample groups. It results in differential expression statistics for each detected transcript, such as the fold change and statistical significance. These statistics are typically visualized using a volcano plot. The genes which are found to be up- or down-regulated can be further visualized as heatmaps or boxplots, for instance.

• As a statistical analysis, this phase of an expression study benefits from the statistical power brought by biological replicates. Three biological replicates per condition is a common “rule-of-thumb” minimum, but it only allows for reliable detection of genes with relatively large expression differences. With a careful experimental design and sufficient sample size, subtler differences can be detected and confounding factors controlled for.

Pathway analysis

PATHWAY ANALYSIS puts genes from a differential expression analysis into broader biological context. Simple pathway analyses compare the up- and down-regulated genes statistically to predetermined gene lists. These lists are annotated to biologically meaningful terms, such as a biological process, signaling pathway or a specific disease.

Such analyses may rely either on over-representation analysis or gene set enrichment analysis, which both result in a list of enriched gene sets with relevant statistics and annotations.

Integrating RNA-seq and epigenomic data

PERFORMING RNA-SEQ AND EPIGENOMIC SEQUENCING (such as ChIP or ATAC-seq) on the same samples enables integrative analyses to study gene regulatory programs genome-wide.

Regulatory connections can be identified between enhancers and their target genes, as well as transcription factors and their targets, building on evidence from both gene expression and the epigenomic status of regulatory elements.