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.
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.
Stefano: Thursday 11-12noon via Zoom ID 918 6420 0042, same password as the lecture
Saleh: Wednesday 11-12noon via Zoom ID 952 6605 3109, same password as the lecture
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)
Seven/eight individual homework to be developed on JupyterLab (50% of the grade)
One programming project (details TBA) (50% of the grade)
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.
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.