CS 234: Computational Methods for the Analysis of Biomolecular Data


  • Sign up for project demo here
  • Homework 3 posted
  • New slides posted
  • Homework 2 posted
  • Sign up for presentation using this sign-up sheet. The title of the paper is necessary to reserve a lot
  • Homework 1 posted
  • Indexing slides posted
  • The first lecture is Monday Sep 26, 2022
  • Overview

    An impressive wealth of data has being ammassed by genome/metagenome/epigenetic projects and other efforts to determine the structure and function of molecular biological systems. This advanced graduate course will focus on a selection of computational problems aimed at automatically analyze biomolecular data.

    Class Meeting

    MW, 2:00pm - 3:30pm, Skye 170

    Office hours

    By appointment via Zoom (email me)

    Preliminary list of topics

  • intro to molecular and computational biology, including biotech tools
  • overview on probability and statistics
  • analysis of 1D sequence data (DNA, RNA, proteins)
  • Space-efficient data structures for sequences
  • Short read mapping (suffix trees, suffix arrays, BWT)
  • Sequence alignment and hidden Markov models (HMM)
  • analysis of 2D data (gene expression data and graphs)
  • clustering algorithms
  • classification algorithms
  • subspace clustering/bi-clustering
  • genetic networks, co-expression networks, metabolic networks, protein-protein interaction graphs
  • Prerequisites

    CS141 (Algorithms) or CS218 (Design and Analysis of Algorithms) or equivalent knowledge. Some programming experience is expected. Students should have some notions of probability and statistics. No biology background is assumed.

    Course Format

    The course will include lectures by the instructor and presentations by the 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 three assignments, mostly of theoretical nature -- although some may require a bit of programming.

    Relation to Other Courses

    This course is intended to complement "CS238: Algorithms in Computational Molecular Biology", and "CS235: Data Mining Concepts".

    References (books)

  • 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, Michael L. Raymer, Fundamental Concepts of Bioinformatics, Benjamin Cummings 2002
  • Warren J. Ewens, Gregory R. Grant, Statistical Methods in Bioinformatics: An Introduction, Springer, 2001
  • Neil C. Jones and Pavel Pevzner, An Introduction to Bioinformatics Algorithms, MIT Press, 2004.
  • Marketa Zvelebil, Jeremy O. Baum, Understanding Bioinformatics, Garland Science, 2007
  • References (papers)

  • Anders Krogh, "An introduction to hidden Markov models for biological sequences" [PDF format]
  • Paolo Ferragina, Giovanni Manzini, "Opportunistic Data Structures with Applications", FOCS 2000 [PDF format]
  • Jeremy Buhler, Uri Keich, Yanni Sun, "Designing Seeds for Similarity Search in Genomic DNA", RECOMB 2003 [PDF format]
  • Avak Kahvejian, John Quackenbush, John F Thompson, "What would you do if you could sequence everything?", Nature Biotechnology, 2008 [PDF format]
  • Michael L. Metzker, "Sequencing technologies - the next generation", Nature Reviews Genetics, 2010 [PDF format]
  • Slides
  • Slides [PDF Format 2slides/page] (Course Overview)
  • Slides [PDF Format 2slides/page] (Intro to Mol Biology)
  • Slides [PDF Format 2slides/page] (Mol Biology Tools)
  • Slides [PDF Format 2slides/page] (Indexing and Searching)
  • Slides [PDF Format 2slides/page] (Probability Models and Inference)
  • Slides [PDF Format 2slides/page] (Bio Networks)
  • Resources

  • CS 234 Fold it! group
  • RNAi animation (Nature Genetics)
  • DNA Molecular animation
  • DNA interactive
  • 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)
  • Projects

  • Project ideas and rules
  • create your CS 234 webpage on Google
  • Kuntal Pal's project
  • Faisal Bin Ashraf's project
  • Aakash Saha's project
  • Omer Eren's project
  • Xianghu Wang's project
  • Rui Yang's project
  • Amun Patel's project
  • Jingong Huang's project
  • Michael Strobel's project
  • Ankit Gupta's project
  • Yuta Nakamura's project
  • JiaJun Yu's project
  • Jay Hemnani's project
  • Xiao Gao's project
  • Priyanshu Sharma's project
  • Jay Hemnani's project
  • Zizhuo Wang's project
  • Mohammed Armughanuddin's project
  • Guoyao Hao's project
  • Rui Ma's project
  • Baoju Wang's project
  • Homework

  • Homework 1 (posted Oct 10, due Oct 19, midnight), LaTeX
  • Homework 2 (posted Oct 19, due Oct 31, midnight), LaTeX
  • Homework 2 solution
  • Homework 3 (posted Oct 31, due Nov 9, midnight), LaTeX
  • Midterm

  • Mock midterm exam
  • Presentation

  • Choose a paper among the Proceedings of RECOMB 2022 (or earlied RECOMB editions) or ISMB 2022 and use the sign up sheet to reserve a spot
  • You can choose a recent computional molecular biology paper from a top journal like Nature, Science, Cell, Genome Biology, Genome Research, but it has to have a significant computational component, and you will need my OK
  • Email me the Powerpoint file the day before the presentation (before 5pm)
  • Give the 15 minutes presentation (make sure you time it correctly, I will stop you at 15 mins, we will reserve a minimum of 2 minutes for questions)
  • Calendar of Lectures

    Week 1
  • Sep 26: Intro, Molecular Biology (1-21)
  • Sep 28: Molecular Biology (22-59)
  • Week 2
  • Oct  3: Molecular Biology (60-94)
  • Oct  5: Molecular Biology (94-end), Molecular Biology Tools (1-42)
  • Week 3
  • Oct 10: Molecular Biology Tools (43-56) [hw1 posted]
  • Oct 12: Molecular Biology Tools (43-56) Indexing (1-23)
  • Week 4
  • Oct 17: Indexing (24-66)
  • Oct 19: Indexing (67-86) [hw1 due][hw2 posted]
  • Week 5
  • Oct 24: Indexing (87-end)
  • Oct 26: Probability Models (1-)
  • Week 6
  • Oct 31: Probability Models () [hw2 due][hw3 posted]
  • Nov  2: Probability Models ()
  • Week 7
  • Nov  7: Networks ()
  • Nov  9: Networks () [hw3 due]
  • Week 8
  • Nov 14: Midterm
  • Nov 16: Presentations (deadline for the PPT file is Nov 15th, 5PM)
  • Week 9
  • Nov 21: Presentations (deadline for the PPT file is Nov 20th, 5PM)
  • Nov 23: Presentations (deadline for the PPT file is Nov 22nd, 5PM)
    Week 10
  • Nov 28: Presentations (deadline for the PPT file is Nov 27th, 5PM)
  • Nov 30: Presentations (deadline for the PPT file is Nov 29th, 5PM)
  • Project Demo (20-25 minutes demo, 5-10 minutes questions, via zoom): here. Please use the zoom meeting ID 972 8807 8095