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.
Course ID: BSBT4001
Course Credits: 4
Course Type: Elective
Pre-requisites:
None
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam.
For details of standard course structure and assessments, visit
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).
WEEK 5
How Do We Compare Biological Sequences? - Dynamic Programming (edit distance, single/multiple sequence alignment).
WEEK 6
Which Animal Gave Us SARS? - Evolutionary Tree Reconstruction (distance-based phylogeny, neighbor-joining algorithm).
WEEK 7
How Did Yeast Become a Winemaker? - Clustering Algorithms (hard and soft k-means).
WEEK 8
How Do We Locate Disease-Causing Mutations? - Combinatorial Pattern Matching (suffix trees/arrays, Burrows-Wheeler transform).
WEEK 9
Why Have Biologists Still Not Developed an HIV Vaccine? - Hidden Markov Models (Viterbi and forward–backward algorithms).
WEEK 10
Was T. rex Just a Big Chicken? - Computational Proteomics (peptide identification and spectral match).
WEEK 11
Which Motifs Are Hidden in a Biological Network? - Randomized Algorithms (colour coding for long paths in graphs).
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.
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