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
technical elective will focus on a selection of algorithms and data structures aimed at
the analysis of biomolecular data. In other words, CS 144 is a Data Science class oriented at the analysis of biomolecular data.
Catalog Description
Introduces fundamental algorithms and data structures for solving analytical problems in molecular biology and genomics. Includes exact and approximate string matching; sequence alignment; genome assembly; and gene and regulatory motifs recognition.
Note: Credit is awarded for one of the following CS 144, CS 234, or CS 238.
TR 9:30am-10:50am, Student Success Center Room 125
Discussions
W 10am-10:50am, Student Success Center Room 125
Office hours
Stefano: Thursday 11-12noon, location: outside SSC (or by appointment)
Sakshar: Thursday 6-7pm, Zoom ID 949 6096 1577 (see canvas announcement for passcode)
Discussion Forum
We will use a Discord server for discussion and questions about CS 144 (and beyond -- religion and politics excluded). The forum will be moderated by the instructor and the TA who will respond to questions, but students are encouraged to help each other via discussion. However, assignment specifics should not be discussed. You will receive an invite to the Discord server via email. If you have joined this class later, please check Canvas. Please be respectful.
Intro to molecular and computational biology, including biotech tools (slides)
Space-efficient data structures for sequences
Short read mapping (suffix tries/trees, suffix arrays, B-W transform)
Sequence alignment (global and local), linear space, multiple
Genome assembly, overlap graphs, de Bruijn graphs
Hidden Markov models, Profile HMM, Viterbi and Baum-Welch learning
Motif finding and Gibbs sampling
Construction of evolutionary trees (phylogeny)
Course Format
Seven/eight individual homework to be developed on JupyterLab (50% of the grade)
One programming project (details TBA) (50% of the grade)
Cheating
We will not tolerate any kind of cheating in this course. Homework and final project 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 I 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.
Late work
Each student is granted five "late days" which can be used (in integer units) on any of the homework. If a more dire situation arises, please contact the instructor.
Homework (in the form of Python notebooks) will be released on Sundays on Canvas (go to Assignments), and they will be due the following Sunday at 11:59pm. Download these Python notebooks on your computer, then upload them into JupyterLab. Homework will have to be completed using CS department’s Juypter Hub server at https://locus.cs.ucr.edu/. Submit your Python notebook on Canvas by the due date. Solutions will be posted on Canvas.
Tuesday, May 24: Motif finding, Evolutionary trees
Thursday, May 26: Evolutionary trees
Sunday, May 29: [hw7 due]
Week 10
Tuesday, May 31: Evolutionary trees, Concluding remarks
Thursday, Jun 2:
Finals' Week
Project demo
Additional 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
Additional resources
Learn how to Fold it! A great game about protein folding that can help the scientific community