In short
NGS data analysis follows a clear pipeline: quality control, trimming, alignment, then either variant calling or expression analysis, and finally interpretation. You can learn it on public datasets and a laptop, and a mentored project turns it into a portfolio piece.
The core workflow
- Quality control of raw reads (FastQC / NanoPlot).
- Trimming and filtering low-quality data.
- Alignment to a reference (or de-novo assembly).
- Variant calling, or expression quantification for RNA-seq.
- Annotation, interpretation and visualisation.
Tools to learn (free)
| Step | Common tools |
|---|---|
| QC | FastQC, MultiQC, NanoPlot |
| Alignment | BWA, minimap2, HISAT2 |
| Variants | GATK, bcftools |
| RNA-seq | Salmon, DESeq2 (R) |
| Workflows | Nextflow, Galaxy |
From tutorial to a real project
Re-analyse a public dataset (SRA/GEO) end to end and write up the result - that single finished analysis is worth more than many half-tutorials. A mentored project adds code review and a defensible methodology.
Manna Biotech offers NGS data-analysis training and support for students and researchers, including short-read and long-read (Nanopore) data, for education and research.
Frequently asked questions
What skills do I need for NGS data analysis?+
Basic Linux, a little Python or R, and an understanding of the workflow (QC, alignment, variants or expression). You can start on public datasets.
Do I need a powerful computer?+
Many steps run on a normal laptop or free cloud tools like Galaxy. Larger genomes may need more memory or a server.
Can I get NGS analysis done for my project?+
Yes - Manna Biotech provides NGS data-analysis support and training, with scientist guidance, for education and research.
Does it cover Nanopore data?+
Yes - both short-read and long-read (Nanopore) workflows are covered where relevant.