School of Bioinformatics · Programming & data
Linux, Shell & HPC for Bioinformatics
Most bioinformatics software runs on Linux servers and computing clusters, not on laptops. This course takes you from your first command to submitting and monitoring batch jobs on a scheduler, with the habits that keep large analyses organised.
Course overview
Linux, Shell & HPC for Bioinformatics is the practical companion to every analysis course in the catalogue. You learn to move around a file system, inspect large sequencing files without opening them, chain tools together with pipes, and write shell scripts that run the same analysis on a hundred samples.
The second half moves to shared computing: connecting securely to a remote server, installing tools with conda and containers, requesting resources from a SLURM scheduler, running array jobs and estimating what an analysis will cost in time, memory and storage.
Learning outcomes
By the end of the course you will be able to:
- Navigate and manage files from the command line
- Inspect, filter and summarise FASTQ, SAM, VCF and GFF files with standard Unix tools
- Write robust Bash scripts with variables, loops and error handling
- Install and pin software with conda and run tools from containers
- Submit, monitor and debug jobs on a SLURM cluster, including array jobs
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- None; comfort with a computer and a willingness to type commands
Best suited to
- Life-science students starting with genomics or structural bioinformatics
- Researchers who have been given a server or cluster account
- Anyone who wants to stop copying commands they do not understand
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Files, directories and paths
- Viewing large files with less, head and tail
- Permissions and safe deletion
Topics
- grep, cut, sort and uniq
- sed and awk for tabular data
- Pipes and redirection
Topics
- Variables, loops and conditions
- Functions, arguments and exit codes
- set -euo pipefail and logging
Topics
- conda, mamba and bioconda
- Containers with Docker and Apptainer
- Recording versions for the methods section
Topics
- SSH keys, rsync and scp
- SLURM: sbatch, squeue, sacct
- Resources, array jobs and dependencies
Topics
- Project layout and README
- Checksums and data integrity
- Estimating compute and storage
Practical sessions and projects
Capstone project
Run a batch analysis on a cluster
Organise a multi-sample project, script its quality control and alignment, run it as a SLURM array job and document it so that someone else can reproduce it.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- First steps on the command line
Organise a project directory for a sequencing study and explore the raw files without opening them in an editor.
- Text processing for biological files
Count reads, extract chromosomes from a VCF and summarise a GFF annotation by feature type in one-line pipelines.
- Shell scripting
Write a script that runs FastQC on every sample, collects the summaries and stops cleanly when an input is missing.
- Software environments
Build a pinned environment for an alignment workflow and run the same tools from a BioContainers image.
- Working on a cluster
Submit an array job that aligns twelve samples in parallel and read the accounting data to tune memory requests.
- Project: a reproducible batch analysis
Deliver a cluster-ready analysis folder that another person can run with one command and audit from its logs.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Escherichia coli K-12 reads and reference: Command-line work on real sequencing files.NCBI SRA and RefSeq NC_000913.3
- nf-core test datasets: Small inputs for batch and array jobs.github.com/nf-core/test-datasets
Core reading
- Shotts W. The Linux Command Line, 2nd edition. No Starch Press, 2019.
- Buffalo V. Bioinformatics Data Skills. O’Reilly, 2015.
- SLURM workload manager documentation (slurm.schedmd.com).
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Linux, Shell & HPC for Bioinformatics, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Linux, Shell & HPC for Bioinformatics
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
Yes. Lab clinics provide a practice environment with a scheduler, and every command also works on a Linux laptop or Windows with WSL2.
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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