Transcriptomics offers a dynamic view of cellular function. RNA sequencing (RNA-Seq) technology generates vast amounts of raw data revealing gene expression levels under specific conditions. This guide focuses on the computational steps necessary to transform those raw sequences into meaningful biological insights. The analysis involves a standardized sequence of steps, starting with quality assessment and concluding with functional interpretation. These steps are achievable using open-source software, allowing researchers to perform sophisticated analyses.
Preparing the Raw Sequence Data
The computational analysis begins with the raw output from the sequencing machine, delivered in the FASTQ file format. This text-based file contains the biological sequence of the read and a corresponding quality score for every nucleotide base. Higher quality scores signify a lower probability that a base has been called incorrectly.
The first stage is quality control (QC) because errors introduced during sequencing can skew subsequent results. QC tools like FastQC analyze the raw FASTQ files and provide a comprehensive report on metrics such as sequence quality distribution and contamination. These reports highlight potential issues, including sequences with low average quality or an overrepresentation of specific short sequences.
After assessment, the raw data requires rigorous cleaning, known as trimming and filtering, to remove technical artifacts. Specialized software, such as Trimmomatic or fastp, eliminates low-quality sections from the ends of reads based on a quality score threshold. These tools also clip off adapter sequences, which are short synthetic DNA fragments added during library preparation.
Short fragments remaining after trimming are removed, as they are less likely to map uniquely and can add noise. This preprocessing ensures that only high-confidence sequence data is retained, enhancing the accuracy of downstream mapping and quantification. The cleaned data should be re-evaluated using FastQC to confirm that trimming successfully mitigated the identified issues.
Alignment and Transcript Quantification
Once sequences are cleaned, the next step is to determine the expression level of each gene or transcript by matching the short reads back to a reference genome. Traditional alignment uses tools like STAR (Spliced Transcripts Alignment to a Reference) to map reads. STAR handles the complexities of RNA-Seq data, such as introns and exons, by performing “spliced alignment.”
After alignment, mapped reads must be counted to determine gene activity, often using a tool like featureCounts. This summarizes the number of reads overlapping with genomic features. The result is the “Count Matrix,” a table where rows represent genes and columns represent samples, containing the raw read counts.
Alignment-free or pseudo-alignment methods, such as Salmon or Kallisto, are an increasingly popular alternative. These methods are faster because they focus on rapidly determining the likelihood that a read originated from a particular transcript, rather than precise mapping. They provide accurate transcript-level abundance estimates directly from the raw reads.
These lightweight tools produce output easily imported into statistical packages, bypassing the need for a separate counting step. The final Count Matrix serves as the input for statistical analysis, providing the data required to test for differences in gene expression between experimental conditions.
Identifying Differentially Expressed Genes
Differential Expression Analysis (DEA) is the core of RNA-Seq analysis, identifying genes that show a significant change in expression between conditions. DEA relies on the Count Matrix and must first account for technical variations, such as sequencing depth. Normalization adjusts the raw counts, ensuring observed differences are due to biological variation rather than library size discrepancies.
Specialized R packages, most commonly DESeq2 and edgeR, are the standard tools for this statistical modeling. Both packages assume gene counts follow a Negative Binomial distribution, which is appropriate for sequencing data. They perform normalization to correct for compositional differences in the RNA libraries.
The analysis uses a statistical test, typically based on a Generalized Linear Model (GLM), to compare gene expression between experimental groups. The output provides the magnitude of change, known as the fold-change (FC), and the statistical confidence, the p-value. The fold-change is usually converted to a log2 fold-change (\(\log_2\text{FC}\)) for simplified interpretation.
Because thousands of tests are performed, a correction for multiple hypothesis testing is mandatory to prevent false positives. This correction yields the adjusted p-value, or False Discovery Rate (FDR). Genes are defined as “significantly differentially expressed” (DEGs) if the adjusted p-value is below a conventional threshold (e.g., 0.05) and the absolute \(\log_2\text{FC}\) exceeds a chosen cutoff (e.g., 1).
Interpreting Biological Function
The final stage involves translating the list of differentially expressed genes (DEGs) into actionable biological knowledge. Researchers need to understand which biological processes or pathways are affected by the experimental condition. Functional enrichment analysis addresses this by determining if predefined sets of genes are overrepresented in the DEG list compared to chance expectation.
Two widely used resources are the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. GO provides a structured set of terms describing gene functions across three domains: Biological Process, Molecular Function, and Cellular Component. KEGG focuses on mapping genes to known molecular interaction and metabolic networks.
Online tools like Metascape, DAVID, or g:Profiler allow users to input DEGs and quickly perform this over-representation analysis. The output highlights GO terms or KEGG pathways statistically enriched among the DEGs, summarizing the biological systems undergoing change. For instance, enrichment in “immune response” suggests that process is specifically modulated by the treatment.
Visualization is a powerful method for interpreting DEA results, converting complex data into intuitive graphics. The Volcano Plot displays the magnitude of gene change (\(\log_2\text{FC}\)) against statistical significance (\(\text{-}\log_{10}\text{(p-value)}\)). Highly changed and significant genes appear in the plot’s peaks, allowing for rapid identification. Heatmaps use color intensity to represent the expression levels of DEGs across all samples, revealing patterns of co-regulation.