Transcriptomic Analysis Based on Bulk RNA-seq Data

Summary

The present protocol establishes a complete pipeline for analyzing the process of bulk RNA-seq from raw data to functional enrichment analysis.

Abstract

Nonalcoholic fatty liver (NAFL) is usually considered a benign condition; however, once it progresses to non-alcoholic steatohepatitis (NASH), patients face a significantly elevated risk of developing end-stage liver disease. Many studies are attempting to elucidate the molecular mechanism underlying the transition from NAFL to NASH. High-throughput sequencing technologies (such as bulk RNA-seq) have provided researchers with a deeper understanding by examining the transcriptome, revealing the expression of molecules, activation of signaling pathways, and other factors associated with disease progression. There is a wealth of open-source data available for researchers to analyze in order to identify potential targets for disease treatment. However, related research is limited by the lack of an efficient and reliable process for upstream analysis of the transcriptome. Here, a highly reproducible and user-friendly upstream analysis and subsequent related differential gene analysis pipeline is provided to achieve standardized processing and deep parsing of private or public data. The pipeline is divided into four steps: (1) quality control of data; (2) gene mapping; (3) differential gene analysis; and (4) functional analysis. This process aims to uncover the molecular mechanisms of disease transformation and assist researchers in screening potential drug targets and therapeutic approaches through the analysis of Bulk RNA-seq data.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease globally, affecting more than a quarter of the population. Its incidence has increased dramatically in recent decades^1,2,3. The growing disease burden, especially its more advanced form, non-alcoholic steatohepatitis (NASH), poses a major global health challenge and a heavy economic burden⁴. The first stage of NAFLD is non-alcoholic fatty liver (NAFL), which is accompanied by inflammation and fibrosis that can progress to NASH. The latter significantly increases the r....

Protocol

For demonstration purposes, the publicly available dataset PRJNA1023502 generated by Lan Bai et al. was used to illustrate each step of both upstream and downstream analyses²⁰. As this dataset originates from the open-access NCBI SRA database, no additional permissions or ethical approvals are required. See the Table of Materials to verify all required software and R-package versions. The publicly available dataset PRJNA1023502 comprises 6 not-NASH, 6 NAFL, and 6 NASH liver RNA-seq samples. In this protocol, the dataset was used to demonstrate all steps of the bulk RNA-seq workflow, including data retrieval from the SRA databas....

Results

The upstream analysis workflow for bulk RNA-seq is illustrated in Figure 1A. This workflow sequentially executes the following key steps on a Linux platform: first, rigorous quality control of raw sequencing data is performed using fastp to remove low-quality reads and adapter sequences; subsequently, HISAT2 aligns high-quality reads to the reference genome, with Samtools converting and sorting the alignment files; finally, FeatureCounts performs gene-level quantification to generate a gene .......

Discussion

Bulk RNA-seq data analysis is characterized as an interdisciplinary task that integrates genomics, bioinformatics, statistics, and computer science. A complete analytical workflow encompasses multiple upstream and downstream steps, including raw data preprocessing, quality control, sequence alignment, gene-level quantification, data normalization, differential expression analysis, and biological interpretation. Among these steps, accurately converting raw sequencing reads into a high-quality gene expression matrix is par.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
biomaRt	Bioconductor	2.64.0	Gene annotation from Ensembl
clusterProfiler	Bioconductor	4.16.0	Functional enrichment analysis
DESeq2	Bioconductor	1.48.1	Differential expression analysis
FactoMineR	AgroParisTech	2.11.0	PCA and multivariate analysis
fastp	OpenGene	1.0.1	Quality control and filtering of FASTQ data
FeatureCounts	Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research	2.0.0	Count the number of reads mapped to each gene for gene expression quantification
ggplot2	Posit	3.5.2	Data visualization
ggrepel	Kamil Slowikowski	0.9.6	Non-overlapping text labels
ggridges	Claus O. Wilke	0.5.6	Create ridgeline plots
HISAT2	Johns Hopkins University	2.2.1	Align the filtered high-quality reads to the reference genome
R	R Core Team	4.5.0	An environment for data computation, analysis, and visualization
RColorBrewer	Erich Neuwirth	1.1.3	Color palettes for plotting
samtools	Large Scale Genomics work stream	1.22.0	Convert and process SAM files for efficient retrieval and access
SRA Toolkit	National Center for Biotechnology Information	3.2.1	Obtain and preprocess raw sequencing data from the NCBI SRA database