Data Analysis Pipeline for RNA-seq Experiments: From Differential Expression to Cryptic Splicing

Abstract

RNA sequencing (RNA-seq) is a high-throughput technology that provides unique insights into the transcriptome. It has a wide variety of applications in quantifying genes/isoforms and in detecting non-coding RNA, alternative splicing, and splice junctions. It is extremely important to comprehend the entire transcriptome for a thorough understanding of the cellular system. Several RNA-seq analysis pipelines have been proposed to date. However, no single analysis pipeline can capture dynamics of the entire transcriptome. Here, we compile and present a robust and commonly used analytical pipeline covering the entire spectrum of transcriptome analysis, including quality checks, alignment of reads, differential gene/transcript expression analysis, discovery of cryptic splicing events, and visualization. Challenges, critical parameters, and possible downstream functional analysis pipelines associated with each step are highlighted and discussed. This unit provides a comprehensive understanding of state-of-the-art RNA-seq analysis pipeline and a greater understanding of the transcriptome. © 2017 by John Wiley & Sons, Inc.

Keywords: RNA-seq; alternative splicing; cryptic splicing; differential gene expression; differential isoform usage.

Figure 1.. Quality check metrics on raw sequence reads from FastQC.

Bar plot of quality score (Phred score) for each base in the reads (a). Line plot showing the distribution of each nucleotide (A, C, G, T) in the sequence reads on each bases (b).

Figure 2.. Visualization inspection of sample clustering.

PCA plot (a) and heatmap on correlation coefficient between samples (b) based on gene expression profiles of the six samples.

Figure 3.. Results of differential gene expression analysis.

MA plot (a) and expression heatmap on the DEGs (adjusted P < 0.01) (b).

Figure 4.. Representative plots from sleuth analysis shiny app.

(a) PCA, (b) Heatmap, (c) Scatterplot, and (d) Density plots.

Figure 5.. Pie chart showing differential isoform usage for gene Gfra1 between WT and MT samples.

The usage of isoform P10382 is decreased from 80.7% in WT to 65.3 % in MT samples. On the other hand, the usage of P18895 increased in MT samples.

Figure 6:. Visualizing splicing events in IGV:

(a) Screen shot showing exon coverage and (b) Sashimi plots with black arrows pointing to junction gains and red arrows pointing to junction losses.