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Degree Level Course

Algorithmic Thinking in Bioinformatics

To prepare students to develop an algorithmic thinking to address key data science challenges in bioinformatics, to acquire knowledge of various problem formulations and algorithm paradigms, which have transformed the field of biomedicine in modern times, to obtain insights into many key bioinformatics algorithms on strings, trees, and graphs, many of which can be applied to other areas as well.

Code BSBT4001
Credits 4 Credits
Type Elective
Prerequisites None
12-Week Roadmap

Course Structure & Syllabus

View Course Videos

For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1
Why computational biology?
WEEK 2
Where in the Genome Does DNA Replication Begin? - Algorithmic warmup (frequent exact/inexact k-mers in a string).
WEEK 3
Which DNA Patterns Play the Role of Molecular Clocks? - Randomized Algorithms (randomized motif search, Gibbs sampling).
WEEK 4
How Do We Assemble Genomes? - Graph Algorithms (Eulerian paths, de Bruijn graphs).
Reading List

Prescribed Books & References

  • Primary textbook. Bioinformatics Algorithms: An Active Learning Approach, 2nd Edition, Vols. 1 and 2. Phillip Compeau, Pavel Pevzner. 2015.
  • Primary Programming Practice platform. Rosalind bioinformatics programming platform. ROSALIND | Problems
  • Additional reference books: Optional 1. Algorithms on Strings, Trees and Sequences. Dan Gusfield. 1997. Optional 2. Biological Sequence Analysis. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison. 1998.
Faculty & Experts

About the Instructors

Manikandan Narayanan

Manikandan Narayanan

Associate Professor , Department of Computer Science & Engineering , IIT Madras

Dr. Manikandan Narayanan, who has joined the Department faculty on January 1, 2018 as an Associate Professor. He received his MS and PhD (2007) from UC Berkeley and his B.E. from CEG, Anna Univ. Until December 2017, he was with NIH, Bethesda, MD, USA.

His research interests are in: Bioinformatics, Computational network biology, Systems biology/genomics in health and disease, Data science.