Introduction
High-throughput sequencing platforms produce billions of short or long nucleotide reads per run. However, raw FASTQ files contain no inherent biological metadata, variant annotations, or functional context. Converting millions of raw reads into definitive biological conclusions requires structured quality metrics, reference alignment, statistical modeling, and database annotation.
Stage 1: Quality Assessment & Preprocessing
Before mapping reads to a reference genome, raw sequencing files undergo thorough quality control using tools such as FastQC and MultiQC. Key checks evaluate per-base sequence quality scores (Phred Q30), base composition bias, GC content distributions, adapter contamination, and overrepresented sequences.
Low-quality bases (Q < 20), sequencing adapters, and poly-G tails are trimmed using fastp or Trimmomatic to prevent false-positive alignment artifacts downstream.
Stage 2: Genome Alignment & Quantification
Filtered reads are aligned to standardized reference genomes (e.g., GRCh38 for human, GRCm39 for mouse) using splice-aware or index-based aligners. BWA-MEM2 or DRAGEN are commonly deployed for DNA variant calling, while STAR or HISAT2 map RNA-seq reads across splice junctions.
Stage 3: Statistical Modeling & Filtering
Once reads are mapped and quantified into count matrices or VCF variant files, rigorous statistical modeling isolates biological signals from experimental noise. Differential expression workflows apply empirical Bayes dispersion shrinkage (DESeq2/edgeR), while variant calling tools filter artifactual calls based on allele fraction, strand bias, and read depth.
Stage 4: Functional Annotation & Biological Context
Statistical hits are contextualized against curated databases like Gene Ontology (GO), KEGG pathways, Reactome, ClinVar, and Ensembl VEP. Pathway enrichment analysis connects isolated gene changes to broad cellular mechanisms, metabolic routes, and disease phenotypes.