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
₹3999
$45
Indian learners pay in INR; international learners are billed in USD.
Paying from outside India? Use this link to complete your payment.
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.