NGS data analysis is a crucial step that transforms raw sequencing reads into meaningful biological insights : 

After a sequencing run, millions of short DNA fragments—called reads—are generated in the form of FASTQ files. The first step is quality control (QC), where tools like FastQC are used to check for low-quality reads, adapter contamination, or sequence duplication. Once filtered, the clean reads are aligned or mapped to a reference genome using software such as BWA or Bowtie2. This alignment phase determines where each read originated within the genome. Following alignment, variant calling tools like GATK or FreeBayes are used to identify genetic differences such as SNPs (single nucleotide polymorphisms), insertions, or deletions. In RNA-Seq workflows, gene expression levels are quantified using tools like HTSeq or featureCounts, and differential expression is analyzed with software such as DESeq2. The final step involves data visualization and interpretation, often using genome browsers (like IGV) or statistical plots to understand biological meaning. Effective NGS data analysis requires not only robust computational tools but also a solid understanding of the experimental context to ensure reliable, reproducible results.