CS 144: Algorithms for Bioinformatics

Overview

An unprecedented wealth of data is being generated by large genome, metagenome, and epigenetic projects, as well as other efforts to determine the structure and function of molecular biological systems. This technical elective focuses on algorithms and data structures for analyzing biomolecular data. In other words, CS 144 is a data science course oriented toward biomolecular data.

Catalog description

• Introduces fundamental algorithms and data structures for solving analytical problems in molecular biology and genomics, including exact and approximate string matching, sequence alignment, genome assembly, and recognition of genes and regulatory motifs.
• Credit is awarded for one of CS 144, CS 234, or CS 238.

Prerequisites

• CS 141
• Solid programming experience, ideally in Python
• Basic probability and statistics
• No biology background is assumed

Course information

Lectures

Tuesday & Thursday, 9:30–10:50 a.m., Boyce Hall 1471.
Note: Lectures will not be recorded.

Discussions

Wednesdays, 3:00–3:50 p.m. over Zoom. Meeting details will be posted on Canvas.

Discussion forum

We will use a Discord server for discussion and questions about CS 144. The instructor will moderate the forum and respond to questions, and students are encouraged to help one another through discussion. Do not discuss assignment-specific solutions. See Canvas for the invitation, and please be respectful.

Office hours

Fridays, 3:00–3:50 p.m. over Zoom. Meeting details will be posted on Canvas. Meetings are also available by appointment.

Required textbook

• Bioinformatics Algorithms, by Phillip Compeau and Pavel Pevzner. See also the companion website.

Topics

• Introduction to molecular and computational biology, including biotech tools
• Space-efficient data structures for sequences
• Short-read mapping: suffix tries and trees, suffix arrays, and the Burrows–Wheeler transform
• Global, local, linear-space, and multiple sequence alignment
• Genome assembly, overlap graphs, and de Bruijn graphs
• Hidden Markov models, profile HMMs, Viterbi, and Baum–Welch learning
• Motif finding and Gibbs sampling
• Construction of evolutionary trees (phylogeny)

Coursework and policies

Course format

• Seven individual homework assignments developed in JupyterLab: 50% of the grade
• One programming project: 50% of the grade

Academic integrity

Cheating will not be tolerated. Homework and the final project must be completed independently without relying on AI tools. You may use only the external sources listed on this page or explicitly allowed by the instructor. Do not submit answers or code that you did not write yourself. Violations will receive a zero on the assignment and may receive a zero in the course, depending on severity, and will be referred to Student Conduct and Academic Integrity Programs. If you are unsure whether something is allowed, ask before submitting.

Late work

Each student receives five late days, usable in whole-day increments on any homework assignment. For more serious circumstances, contact the instructor.

Slides and grades

Slides and grades will be posted on Canvas.

Homework

Homework will be released as Python notebooks on Sundays in the Assignments area of Canvas and will be due the following Sunday at 11:59 p.m. Download each notebook, upload it to the department's JupyterLab server, and complete your work there. Submit the finished notebook on Canvas by the deadline. Solutions will be posted on Canvas.

Additional references

• Richard Durbin, A. Krogh, G. Mitchison, and S. Eddy, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1999.
• Dan Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology, Cambridge University Press, 1997.
• Dan E. Krane and Michael L. Raymer, Fundamental Concepts of Bioinformatics, Benjamin Cummings, 2002.
• Neil C. Jones and Pavel Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004.
• Marketa Zvelebil and Jeremy O. Baum, Understanding Bioinformatics, Garland Science, 2007.

Additional resources

• Foldit, a protein-folding game that contributes to scientific research
• Genomic Data Science Specialization on Coursera
• Bioconductor for Genomic Data Science on Coursera
• Genome Sequencing: Bioinformatics II on Coursera
• Introduction to Genomics from NHGRI
• Fundamentals of Biology from MIT OpenCourseWare
• Pavel Pevzner's bioinformatics courses on Coursera