- RNA-seq data analysis is a multi-step computational process that transforms raw sequencing reads into meaningful biological insights about gene expression, transcript structure, and regulation. The analysis pipeline generally involves quality control, read alignment or transcript assembly, quantification, normalization, and statistical testing for differential expression, along with downstream functional interpretation.
- The first step is quality control (QC), which assesses the quality of raw sequencing reads using tools like FastQC. This step helps identify issues such as adapter contamination, low-quality bases, or sequence biases. Low-quality reads or bases can be trimmed or filtered using tools like Trimmomatic or Cutadapt to ensure reliable downstream analysis.
- Next, the cleaned reads are subjected to alignment or mapping. In this step, reads are aligned to a reference genome or transcriptome using aligners such as HISAT2, STAR, or TopHat2. Alternatively, in cases where a reference genome is unavailable, de novo transcriptome assembly tools like Trinity are used. Accurate mapping is crucial for determining which genes or transcripts the reads originate from.
- Once the reads are mapped, quantification is performed to estimate the abundance of transcripts. Tools such as HTSeq, featureCounts, Salmon, or Kallisto generate count data representing the number of reads mapped to each gene or transcript. These raw counts are then subject to normalization, which adjusts for sequencing depth and other technical variations, making expression values comparable across samples. Common normalization methods include TPM (Transcripts Per Million), RPKM/FPKM, and methods embedded in tools like DESeq2 or edgeR.
- The normalized data are used to perform differential gene expression analysis. Statistical tools such as DESeq2, edgeR, or limma-voom identify genes whose expression levels significantly differ between experimental conditions or groups. These tools account for biological variability and correct for multiple testing to control false discovery rates (FDR).
- Finally, functional enrichment analysis and pathway analysis are conducted to interpret the biological significance of differentially expressed genes. Tools like DAVID, GOseq, GSEA (Gene Set Enrichment Analysis), or KEGG pathway analysis help identify overrepresented biological processes, molecular functions, and pathways.
- Overall, RNA-seq data analysis is a robust and flexible approach for understanding gene expression changes, discovering new transcripts, and uncovering regulatory mechanisms. It plays a critical role in studies ranging from basic biology to disease research and personalized medicine.
