School of Bioinformatics · Programming & data
Python for Bioinformatics
Python is the most widely used language in bioinformatics. This course teaches it through biological problems from the first lesson, so every exercise reads, transforms or plots real sequence and expression data.
Course overview
Python for Bioinformatics teaches programming through the problems a biologist actually meets: reading sequence files, counting and comparing, cleaning tables and producing figures. No prior programming is assumed; by the end you write, test and share small analysis tools of your own.
You first write the parsers and calculations yourself, so you understand what the libraries do, and then switch to Biopython and pandas for real-scale work. Good practice is part of the syllabus from the start: functions with clear inputs and outputs, tests with pytest and version control with Git.
Learning outcomes
By the end of the course you will be able to:
- Write clear Python scripts, functions and modules
- Parse FASTA, FASTQ and GenBank files with and without Biopython
- Clean, join and summarise tabular data with pandas
- Produce publication-quality figures with matplotlib and seaborn
- Structure, test and share a small analysis project
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
- Basic molecular biology
- No prior programming
Best suited to
- Life-science students with no coding background
- Researchers who reuse scripts and want to understand them
- Students preparing for the NGS, RNA-seq or drug-discovery courses
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Variables, strings and lists through DNA sequences
- Loops and conditions: GC content and motif search
- Functions and modules
Topics
- Reading FASTA and FASTQ by hand
- Dictionaries for codon tables and k-mer counts
- Writing results to CSV
Topics
- SeqIO and SeqRecord
- Translation, ORFs and reverse complements
- Querying NCBI with Entrez
Topics
- DataFrames for expression tables
- Filtering, grouping and joining
- Handling missing values
Topics
- matplotlib and seaborn fundamentals
- Heatmaps, volcano plots and distributions
- Figure design for journals
Topics
- Command-line arguments
- Testing with pytest
- Sharing code on GitHub
Practical sessions and projects
Capstone project
Build a sequence-analysis toolkit
Write and test a small command-line program that validates FASTA input, reports composition, finds ORFs and plots GC content along each sequence.
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.
- Python essentials
Compute GC content, reverse complements and motif counts in a notebook, then turn the code into reusable functions.
- Working with files
Write a FASTA and FASTQ reader from scratch, count k-mers with a dictionary and export per-record statistics to CSV.
- Biopython
Fetch GenBank records with Entrez, translate coding sequences and find open reading frames in a bacterial genome segment.
- Data analysis with pandas
Load the airway count table, filter low-count genes, join gene annotations and summarise expression by condition.
- Visualisation
Rebuild a volcano plot and a heatmap from a published table and revise them until they meet a journal’s figure guidelines.
- Project: a reusable sequence toolkit
Package the course code as a command-line tool with arguments, tests and a README, and publish it to a Git repository.
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 MG1655 genome: Parsing, k-mer counting and ORF finding.NCBI RefSeq NC_000913.3
- airway RNA-seq count table: pandas exercises on real expression data.Bioconductor airway package (GEO GSE52778)
- GenBank records retrieved live: Programmatic data access.NCBI Entrez E-utilities
Core reading
- Cock PJA et al. (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422–1423.
- McKinney W. Python for Data Analysis, 3rd edition. O’Reilly, 2022.
- Biopython Tutorial and Cookbook (online).
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 Python 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 Python 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
Either works. Python is general-purpose and dominant in genomics pipelines and machine learning; R is strongest for statistics and Bioconductor. Many bioinformaticians use both, and the learning paths show where each one fits.
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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