RNA sequencing (RNA-Seq) is a technology that allows researchers to quantify and identify RNA molecules in a biological sample. It provides a detailed snapshot of the transcriptome, the complete set of RNA transcripts in a cell or organism at a specific time. Analyzing RNA-Seq data is a multi-step computational process used to uncover insights into biological phenomena such as disease mechanisms, developmental processes, and cellular functions. This approach helps understand how gene activity changes under different conditions.
Preparing Raw Data
The initial phase of RNA-Seq data analysis involves preparing raw sequencing reads to ensure their quality for downstream steps. Raw sequencing data, typically in FASTQ format, can contain errors or technical artifacts. Assessing the quality of these raw reads is an important first step, often performed using tools like FastQC.
FastQC generates reports highlighting potential issues like low-quality bases, adapter contamination, and unusual sequence content. These reports help identify problems from the sequencing process or initial library preparation.
Following quality assessment, trimming and filtering steps remove undesirable elements. Tools like Trimmomatic excise adapter sequences, artificial DNA fragments added during library preparation. Low-quality bases at read ends are also removed to improve accuracy. The goal is to produce clean, high-quality reads, necessary for accurate gene expression quantification.
Quantifying Gene Expression
After raw sequencing data is cleaned, the next step involves determining where reads originated within the genome and quantifying individual gene expression levels. This process begins with alignment, where high-quality reads are mapped to a reference genome or transcriptome. Specialized aligners, such as STAR (Spliced Transcripts Alignment to a Reference) or HISAT2, are used because RNA-Seq reads often span splice junctions, regions where non-coding introns are removed from RNA.
STAR is efficient in handling these splice junctions. After alignment, quantification involves counting aligned reads to determine transcript or gene abundance. Tools like featureCounts summarize reads that map to each gene, providing raw count data. Alternatively, methods like Salmon or Kallisto use pseudo-alignment, which quantifies transcript abundance without full alignment, offering a faster approach. These count values measure gene expression, indicating how actively each gene is transcribed in the biological sample.
Identifying Significant Changes
After quantifying gene expression, the focus shifts to identifying genes whose expression levels significantly differ between experimental conditions. This process, known as differential expression analysis, requires statistical consideration. Raw gene counts must first undergo normalization to account for technical variations, such as differences in sequencing depth or RNA composition between samples. Normalization ensures that observed differences in gene counts are biological rather than technical artifacts, allowing for accurate comparisons.
Statistical models are then applied to these normalized counts to pinpoint differentially expressed genes. Popular R packages for this purpose include DESeq2 and edgeR.
These tools use statistical distributions, such as the negative binomial distribution, to model gene counts and identify genes with statistically significant changes in expression. The analysis output includes fold change values, which represent the magnitude of expression difference, and p-values. Adjusted p-values, often referred to as False Discovery Rate (FDR), are used to correct for multiple testing, providing a more reliable measure of statistical significance. Differentially expressed genes show a meaningful change in activity levels between conditions, indicating their involvement in the biological process.
Uncovering Biological Meaning
The final stage of RNA-Seq data analysis involves transforming lists of differentially expressed genes into meaningful biological insights. This step moves beyond individual gene changes to understand broader cellular processes and pathways. Functional enrichment analysis is a common approach, where identified genes are assessed for over-representation in known biological categories.
Tools like GOseq, DAVID, or GSEA (Gene Set Enrichment Analysis) determine if specific Gene Ontology (GO) terms, biological pathways, or disease associations are significantly enriched within the set of differentially expressed genes. This analysis helps infer the biological functions or pathways most affected by experimental conditions. Visualizing results is an important part of interpretation, making complex data more accessible. Common visualization methods include heatmaps, which display gene expression patterns across samples; volcano plots, which highlight genes with significant fold changes and p-values; and pathway diagrams, which illustrate affected biological networks. Integrating RNA-Seq data with other “omics” data, such such as proteomics or metabolomics, can provide a more holistic understanding of biological systems.