CS 234: Computational Methods for the Analysis of Biomolecular Data
Spring 2024
Overview
An unprecedented wealth of data is being generated by large genome/metagenome/epigenetic projects and other efforts to determine the structure and function of molecular biological systems. This advanced graduate class will focus on a selection of algorithms and data structures aimed at the analysis of biomolecular data.
Catalog Description
A study of computational and statistical methods aimed at automatically analyzing, clustering, and classifying biomolecular data. Includes combinatorial algorithms for pattern discovery; hidden Markov models for sequence analysis; analysis of expression data; and prediction of the three-dimensional structure of RNA and proteins.
Note: Credit is awarded for one of the following CS 144, CS 234, or CS 238.
MW 12:30pm-1:50pm, Student Success Center, Room 125
Office hours
Stefano: TBA (or by appointment), Zoom meeting: TBA
Saleh: TBA (or by appointment), Zoom meeting: TBA
Preliminary list of topics
Intro to molecular and computational biology, including biotech tools
String matching and approx string matching (Z-algorithm, KMP, Boyer Moore)
Space-efficient data structures for sequences (suffix tries/trees, suffix arrays, B-W transform)
Hidden Markov models, Profile HMM, Viterbi and Baum-Welch learning
Motif finding and Gibbs sampling
References
(HMMs) Richard Durbin, A. Krogh, G. Mitchison, and S. Eddy, Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids, Cambridge University Press, 1999.
(Suffix Trees) Dan Gusfield, Algorithms on Strings, Trees and Sequences - Computer Science and Computational Biology, Cambridge University Press, 1997.
(Algorithms) Dan E. Krane, Michael L. Raymer, Fundamental Concepts of Bioinformatics, Benjamin Cummings 2002
(Algorithms) Neil C. Jones and Pavel Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004
(Algorithms) Marketa Zvelebil, Jeremy O. Baum, Understanding Bioinformatics, Garland Science, 2007
Course Format
The course will include lectures by the instructor and possibly guest lectures from senior PhD students.
Students are expected to study the material covered in class. In addition to selected chapters from
some of the books listed below, there may be handouts of research papers. There will be several homework assignments,
mostly of theoretical nature -- although some may require a bit of programming. There will be a midterm and a final.
Cheating
We will not tolerate any kind of cheating in this course. Homework are to be completed on your own. The only external sources allowed are those mentioned above or by the instructor throughout the course. If you have a doubt or question, please just ASK. As per standard UCR policy, you may not submit answers (written or programming) to problem sets that contain material you did not produce yourself for the express purpose of this offering of this course. If we find that you have submitted work that is not your own or is work you submitted in a different course, I will assign you a zero on that assignment (and possibly a zero on the entire course, depending on the severity), and I will forward the case to Student Conduct and Academic Integrity Programs for campus-level consideration.
Homework will be released on Canvas. Homework will have to be submitted on Canvas. Each student is granted three "late days" which can be used (in integer units) on any of the homework. If a more dire situation arises, please contact the instructor.
Calendar
Week 1
Monday, Apr 1: Intro, Molecular Biology
Wednesday, Apr 3: Molecular Biology
Week 2
Monday, Apr 8:
Wednesday, Apr 10:
Week 3
Monday, Apr 15:
Wednesday, Apr 17:
Week 4
Monday, Apr 22:
Wednesday, Apr 24:
Week 5
Monday, Apr 29:
Wednesday, May 1:
Week 6
Monday, May 6:
Wednesday, May 8:
Week 7
Monday, Apr 13:
Wednesday, Apr 15:
Week 8
Monday, May 20:
Wednesday, May 22:
Week 9
Monday, May 27:
Wednesday, May 29:
Week 10
Monday, Jun 3:
Wednesday, Jun 5:
Finals' Week
Final
Additional resources
Learn how to Fold it! A great game about protein folding that can help the scientific community