CS 144: Algorithms for Bioinformatics

Winter 2024


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. Thistechnical 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.
  • Prerequisites

  • CS 141
  • Solid programming experience (ideally Python)
  • Some basic notions of probability and statistics
  • No biology background is assumed
  • Instructors

  • Stefano Lonardi, email, office MRB 3130
  • Faisal Bin Ashraf, email, office MRB 3rd floor (cubicles)
  • Class Meeting

  • TR 12:30pm-1:50pm, Physics, Room 2104
  • Discussions

  • W 8:00pm-8:50pm, Sproul Hall, Room 2340
  • Office hours

  • Stefano: Fridays 1-2pm (or by appointment), Zoom meeting ID 976 1037 9494, check Canvas for password
  • Faisal: Fridays 12noon-1pm (or by appointment), Zoom meeting ID 997 8978 5948, check Canvas for password
  • 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. Please check Canvas for details about Discord. Please be respectful.

    Required Textbook

  • Bioinformatics Algorithms (UC Riverside 2024 edition) by Phillip Compeau (CMU) and Pavel Pevzner (UCSD), 2020. Companion website
  • Preliminary list of topics

  • Intro to molecular and computational biology, including biotech tools
  • 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 individual homework to be developed on JupyterLab (50% of the grade)
  • One programming project (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.


    Slides will be posted on Canvas.


    Grades will be posted on Canvas.


    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.


    Week 1
  • Tuesday, Jan 9: Intro, Molecular Biology
  • Thursday, Jan 11: Molecular Biology
  • Sunday, Jan 14: [hw1 posted]
  • Week 2
  • Tuesday, Jan 16: Molecular Biology
  • Thursday, Jan 18: Read Mapping
  • Sunday, Jan 21: [hw1 due], [hw2 posted]
  • Week 3
  • Tuesday, Jan 23: Read Mapping
  • Thursday, Jan 25: Read Mapping
  • Sunday, Jan 28: [hw2 due], [hw3 posted]
  • Week 4
  • Tuesday, Jan 30: Discussion of projects
  • Thursday, Feb 1: Sequence Alignment
  • Sunday, Feb 4: [hw3 due], [hw4 posted]
  • Week 5
  • Tuesday, Feb 6: Sequence Alignment
  • Thursday, Feb 8: Genome Assembly
  • Sunday, Feb 11: [hw4 due]
  • Week 6
  • Tuesday, Feb 13: Genome Assembly
  • Thursday, Feb 15: HMM
  • Sunday, Feb 18: [hw5 posted]
  • Week 7
  • Tuesday, Feb 20: HMM
  • Thursday, Feb 22: HMM
  • Sunday, Feb 25: [hw5 due], [hw6 posted]
  • Week 8
  • Tuesday, Feb 27: Motif finding
  • Thursday, Feb 29: Motif finding
  • Sunday, Mar 3: [hw6 due], [hw7 posted]
  • Week 9
  • Tuesday, Mar 5: Evolutionary trees
  • Thursday, Mar 7: Evolutionary trees
  • Sunday, Mar 10: [hw7 due]
  • Week 10
  • Tuesday, Mar 12: Evolutionary trees, Concluding remarks
  • Thursday, Mar 14:
  • Sunday, Mar 17: [project due]
  • 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
  • Genomic Data Science Specialization (Coursera)
  • Bioconductor for Genomic Data Science (Coursera)
  • Genome Sequencing (Bioinformatics II) (Coursera)
  • Introduction to Genomics (NHGRI)
  • Fundamentals of Biology (on-line course)
  • Pevzner's bioinformatics courses (Coursera)