School of Scientific Computing & Informatics · Algorithms & software engineering
Algorithms for Bioinformatics
Aligners, assemblers and gene finders are built on a small set of powerful algorithmic ideas. This course teaches those ideas by implementing them, so that you understand the tools you use and can design new ones.
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Course overview
Algorithms for Bioinformatics covers dynamic programming for sequence alignment, graph algorithms for genome assembly, string indexing with suffix arrays and the Burrows–Wheeler transform, and probabilistic models such as hidden Markov models.
Each module pairs theory with implementation in Python and a complexity analysis, then compares your implementation with the production tool. The course is the bridge between using bioinformatics software and writing it.
Learning outcomes
By the end of the course you will be able to:
- Implement global, local and affine-gap alignment with dynamic programming
- Build de Bruijn graphs and find Eulerian paths for assembly
- Construct suffix arrays, the BWT and an FM-index for fast search
- Implement HMM decoding for gene and profile models
- Analyse time and memory complexity and profile real code
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- Python programming
- Basic discrete mathematics
Best suited to
- Computer-science and engineering students entering biology
- Bioinformaticians who want to build tools
- Students preparing for research or graduate study in computational biology
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Needleman–Wunsch and Smith–Waterman
- Affine gaps
- Banded and linear-space alignment
Topics
- Overlap and de Bruijn graphs
- Eulerian paths
- Errors and repeats
Topics
- Suffix tries, trees and arrays
- Burrows–Wheeler transform
- FM-index and backward search
Topics
- Markov chains and CpG islands
- Hidden Markov models
- Viterbi and forward–backward
Topics
- Complexity and benchmarks
- Profiling Python
- When to drop to C, Rust or WebAssembly
Topics
- Reading the paper
- Implementation
- Validation
Practical sessions and projects
Capstone project
Implement a published algorithm
Implement, validate and benchmark an algorithm from the bioinformatics literature, and explain where it beats or loses to existing tools.
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.
- Alignment by dynamic programming
Implement affine-gap alignment and verify it against a reference implementation.
- Graphs and assembly
Assemble a simulated genome from k-mers and study how errors break the graph.
- Indexing genomes
Build an FM-index for a bacterial genome and search millions of reads against it.
- Probabilistic models
Implement Viterbi decoding to find CpG islands in a genomic sequence.
- Efficiency in practice
Profile and speed up your aligner and report the gain.
- Project: implement a published method
Implement an algorithm from a paper, test it against known answers and benchmark it.
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
- Simulated genomes and reads: Testing with known truth.Generated in class
- Escherichia coli genome: Indexing at realistic scale.NCBI RefSeq
Core reading
- Compeau P, Pevzner P. Bioinformatics Algorithms: An Active Learning Approach, 3rd edition. Active Learning Publishers, 2018.
- Durbin R, Eddy S, Krogh A, Mitchison G. Biological Sequence Analysis. Cambridge University Press, 1998.
- Langmead B, Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods 9:357–359.
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 Algorithms 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 Algorithms 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
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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