IITM BS Algorithmic Thinking in Bioinformatics (BSBT4001): Syllabus and Tips

Algorithmic Thinking in Bioinformatics (BSBT4001) is a 4 credit elective at the degree level of the IITM BS in Data Science. Its code starts with 4, so it is a level 4 course. Neither the course page nor the handbook lists a prerequisite. You only need to have reached the degree level. The course teaches classic algorithms on strings, trees and graphs through real questions from biology.

The course page names Manikandan Narayanan, Associate Professor in the Department of Computer Science and Engineering at IIT Madras, as the instructor.

What you learn

Each week opens with a biology question and answers it with one type of algorithm.

• Weeks 1 to 3: patterns in DNA. Why biology needs computing. Then finding short DNA strings that repeat often, with or without small mismatches. Then randomised search, including Gibbs sampling, to find hidden patterns (motifs).
• Weeks 4 to 6: assembling and comparing sequences. Rebuilding a genome from small pieces using graph paths (Eulerian paths and de Bruijn graphs). Comparing two or more sequences with dynamic programming (edit distance and alignment). Building evolutionary trees from distances with the neighbour joining method.
• Weeks 7 to 9: grouping and fast searching. Hard and soft k-means clustering. Fast pattern matching with suffix trees, suffix arrays and the Burrows-Wheeler transform. Hidden Markov models with the Viterbi and forward-backward algorithms.
• Weeks 10 and 11: proteins and networks. Identifying peptides by matching spectra. A randomised method called colour coding for finding long paths in biological networks.

The course page says 12 weeks of coursework but lists topics for only 11.

The main textbook is Bioinformatics Algorithms: An Active Learning Approach (2nd edition) by Phillip Compeau and Pavel Pevzner. The main practice platform is Rosalind, a site of bioinformatics programming problems. Two optional reference books are also listed.

How it is assessed

The course page lists the standard pattern: 12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes and 1 in-person invigilated end term exam. The exact weight of each part is in the grading document for your term.

Where it counts

• BS level electives. The handbook tags this course BD/BP. BD appears to mean the data science stream and BP the programming stream. The BS level needs 2 level 4 or higher courses in each stream. The handbook does not say which stream a BD/BP course fills, so confirm with support before you count on it. See the BS degree level.
• BSc level electives. The handbook's BSc fee options include level 4 electives, so you may take it during the BSc level too.
• Minors. It is not part of any minor in the handbook.
• Terms. The March 2026 course table marks it as running in May 2026 and January 2027, but not September 2026.

Who finds it hard

If you have not studied biology, the terms will be new, but the harder part is the algorithms. Dynamic programming, graph paths and string matching come back often. If you passed PDSA but have forgotten those weeks, weeks 4, 5 and 8 will feel heavy. Hidden Markov models and Gibbs sampling also need comfort with probability.

How to prepare

1. Revise PDSA first. Go over graph algorithms, dynamic programming and string matching from PDSA (BSCS2002).
2. Learn the biology words. DNA, genome, gene, protein, mutation. A few minutes on each before week 1 saves lecture time.
3. Work one example by hand each week. For example, the edit distance between ACGT and AGT is 1, because deleting the C turns one into the other. Fill the dynamic programming table yourself before you code it.
4. Solve Rosalind problems in step with the lectures. The course page does not name a language. Use one you can already write quickly.
5. Keep a one-page sheet per algorithm with its input, output, main idea and one small example.

What to take before or after

The clustering and probability weeks are easier if you remember Machine Learning Techniques from the diploma. After this course, Big Data and Biological Networks (BSBT4002) looks at biology through networks, and Advanced Algorithms (BSCS4021) goes deeper into algorithm design. The full elective list is in IITM BS Data Science electives.

Common questions

Do I need to know biology for BSBT4001?

No biology course is listed as a prerequisite. Each week is framed around a biology question. Learning the basic terms before the term starts will make the lectures easier to follow.

Is BSBT4001 offered every term?

No. The handbook's table marks it as running in May 2026 and January 2027, not in September 2026. Offering also depends on registration numbers.

Will this course involve coding?

Yes. The course page names Rosalind as the main programming practice platform. Expect to turn each algorithm into working code, not just learn it on paper.

Can BSBT4001 count as a level 4 programming course for the BS degree?

It carries both the BD and BP tags, but the handbook does not say how such courses are counted. Ask support which stream it will fill for you before you plan around it.