NGS & Linux Workflow: DNA-Seq Data Analysis - recorded course
Master the Linux environment to architect robust bioinformatics pipelines for clinical and research-grade DNA sequencing. Leverage AI-driven command-line tools and shell scripting to automate the journey from raw reads to genomic variants.
- 4.0/5
- English
- Updated Aug 2026
About this course
In the era of Big Data genomics, the ability to navigate the Linux terminal is the ultimate superpower for researchers. This specialized course, DNASeq + Linux, provides a hands-on immersion into the computational backbone of Next-Generation Sequencing (NGS) analysis. You will move beyond simple point-and-click interfaces, learning to manage directories, handle large FASTQ files, and execute multi-tool pipelines in a high-performance computing environment. We integrate Artificial Intelligence (AI) through intelligent terminal assistants that help optimize your Bash scripting and debug complex bioinformatics errors in real-time. The curriculum follows a rigorous DNA-Seq workflow—from quality control and reference genome alignment to AI-accelerated variant calling. By mastering the command-line interface (CLI), you will learn how to automate repetitive tasks and ensure your research is scalable and reproducible. Whether you are aiming for a career in Genomics, Precision Medicine, or Biotechnology, this course provides the technical grit required to lead in the digital laboratory.
What you will learn
Skills you will gain
Course curriculum
1 moduleWhat you need to start
- A basic understanding of Genetics and DNA structure.
- Access to a computer (Mac or Windows with WSL2 installed).
- No prior Linux or coding experience is required—we start from the very first command
Who this course is for
- Wet-lab Biologists who want to break free from GUI constraints and gain computational independence.
- Bioinformatics Students needing a solid foundation in Linux before tackling advanced omics.
- Genomic Scientists aiming to automate their research workflows for higher throughput.
- Software Engineers transitioning into the Life Sciences and Biotech industries.