RNA sequencing (RNA-Seq) is a powerful NGS-based technique used to understand gene expression at the cellular level. In this article, we will explore the working principles of RNA-Seq, bioinformatics analysis steps, and its significance in research in detail.

What is RNA-Seq and Why is it Used?

RNA-Seq is an NGS method used to profile the transcriptome within a cell. Compared to traditional microarray techniques, it is more sensitive, comprehensive, and has a wider dynamic range.

Main Applications of RNA-Seq

• Gene expression analysis (determining which genes are actively used in cells and to what extent)
• Detection of alternative splice variants
• Discovery of non-coding RNAs
• Identification of disease biomarkers
• Cancer and neurological disease research

RNA-Seq Analysis Steps

The process of obtaining raw data from RNA-Seq and deriving biological meaning consists of several key steps.

Library Preparation

RNA samples are first purified through mRNA isolation or rRNA depletion. Then, they are processed for NGS by cDNA synthesis, fragmentation, and adapter ligation.

Raw Data Quality Control

NGS output is in FASTQ format, which requires quality control.

Alignment of Reads to the Reference Genome

RNA-Seq data must be aligned to the reference genome or transcriptome.

Quantification of Gene Expression

Expression levels are calculated using metrics such as read count, FPKM/TPM/RPKM.

Differential Gene Expression (DGE) Analysis

DGE analysis is conducted to compare gene expression levels between different groups, such as diseased and healthy cells.

Functional Interpretation of RNA-Seq Data

Differential gene lists obtained from RNA-Seq analysis must undergo functional analysis to gain biological insights.

Gene Ontology (GO) Analysis

GO analysis is used to understand how genes function in biological processes (BP), cellular components (CC), and molecular functions (MF).

KEGG Pathway Analysis

Used to determine the cell signaling and metabolic pathways in which genes are involved.

Protein-Protein Interaction (PPI) Analysis

PPI analysis is used to understand the interaction networks of differentially expressed genes within the cell.

Its Use in Clinical Research

RNA-Seq is used in medical research to identify genetic biomarkers.

RNA-Seq in Cancer Research

• Transcriptome profiling of the tumor microenvironment
• Detection of cancer mutations (fusion genes, splice variants)
• Biomarker discovery for immunotherapy

Future Perspectives: RNA-Seq and Artificial Intelligence

Machine learning and artificial intelligence are increasingly being utilized in RNA-Seq data analysis.

Conclusion: Understanding Gene Expression Profiles with RNA-Seq

RNA-Seq has revolutionized modern genomic research. With bioinformatics analyses, it is now possible to understand how genes are regulated, their relationship with diseases, and their roles in biological systems.
Tzec-Interián, J. A., González-Padilla, D., & Góngora-Castillo, E. B. (2025). Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses. Quantitative Biology, X(X), Article 78. https://doi.org/10.1002/qub2.78