Bi/BE/CS 183, Winter 2024

Introduction to Computational Biology and Bioinformatics
Instructors Richard Murray (CDS/BE) Lectures: MWF 11-11:55a, room TBD Office hours: Wed, 3-3:45 pm, Annenberg treehouse lounge	Teaching Assistants Tara Chari, Meichen Fang, TBD Office hours: TBD

This is the course homepage for Bi/BE/CS 183, Winter 2024. This course closely follows the Winter 2023 course.

Catalog Description

Bi/BE/CS 183. Introduction to Computational Biology and Bioinformatics. 9 units (3-0-6): second term. Prerequisites: Bi 8, CS 2, Ma 3; or BE/Bi 103 a; or instructor's permission. Biology is becoming an increasingly data-intensive science. Many of the data challenges in the biological sciences are distinct from other scientific disciplines because of the complexity involved. This course will introduce key computational, probabilistic, and statistical methods that are common in computational biology and bioinformatics. We will integrate these theoretical aspects to discuss solutions to common challenges that reoccur throughout bioinformatics including algorithms and heuristics for tackling DNA sequence alignments, phylogenetic reconstructions, evolutionary analysis, and population and human genetics. We will discuss these topics in conjunction with common applications including the analysis of high throughput DNA sequencing data sets and analysis of gene expression from RNA-Seq data sets.

Lecture Schedule

Date	Topic	Reading	Homework
Week 1 3 Jan 5 Jan	Course Introduction Overview of computational biology Logistics for the course Overview of scRNA-seq	Wi 2024 lecture slides: Mon, Wed, Fri Wi 2023 lecture slides: Lecture 1: Introduction to computational biology of single-cell RNA-seq Lecture 2: Single-cell RNA-seq technology	HW #1 Out: 3 Jan Due: 10 Jan
Week 2 8 Jan 10 Jan 12 Jan	Correlation and regresssion Linear and logistic regression, least squares Random variables, covariance, correlation Exploratory data analysis		HW #2 Out: 10 Jan Due: 17 Jan
Week 3 15 Jan 17 Jan 19 Jan	Dimensionality reduction Singular value decomposition (SVD), principal components analysis (PCA) Clustering and data visualization (PCA, t-SNE, UMAP)		HW #3 Out: 17 Jan Due: 24 Jan
Week 4 22 Jan 24 Jan 26 Jan*	Expectation maximization (EM) Maximum likelihood estimation (MLE) Clustering via EM Gaussian mixture models		HW #4 Out: 24 Jan Due: 31 Jan
Week 5 29 Jan 31 Jan 2 Feb	Read alignment and modeling counts Read alignment via EM String algorithms, suffix trees Modeling counts, zero-inflated negative binomial distributions		HW #5 Out: 31 Jan Due: 7 Feb
Week 6 5 Feb 7 Feb 9 Feb	Variance stabilization Normalization, log1p transformations		HW #6 Out: 7 Feb Due: 14 Feb
Week 7 12 Feb 14 Feb 16 Feb	Differential analysis Hypothesis testing		HW #7 Out: 14 Feb Due: 21 Feb
Week 8 19 Feb 21 Feb 23 Feb*	Hidden Markov models Global and local alignment (Needleman-Wunsch, Smith-Waterman) Dynamic programming		HW #8 Out: 21 Feb Due: 28 Feb
Week 9 26 Feb 28 Feb 1 Mar	Markov processes Continuous-time Markov chains Stochastic simulation algorithm Bursty gene expression		HW #9 Out: 28 Feb Due: 6 Mar
Week 10 4 Mar 6 Mar 8 Mar	Machine learning Overview of alphaFold, ESM2		Final Out: 8 Mar Due: 15 Mar

Grading

The final grade will be based on homework sets and a final exam:

• Homework (70%): Homework sets will be handed out weekly and due on Wednesdays by 11 am using GradeScope. Each student is allowed up to two extensions of no more than 2 days each over the course of the term. Homework turned in after Friday at 11 am or after the two extensions are exhausted will not be accepted without a note from the health center or the Dean. Python code is considered part of your solution and should be printed and turned in with the problem set (whether the problem asks for it or not).

The lowest homework set grade will be dropped when computing your final grade.

• Final exam (30%): The final exam will be handed out on the last day of class (8 Mar) and due at the end of finals week. It will be an open book exam and computers will be allowed.

Collaboration Policy

Collaboration on homework assignments is encouraged. You may consult outside reference materials, other students, the TA, or the instructor, but you cannot consult homework solutions from prior years and you must cite any use of material from outside references. All solutions that are handed in should be written up individually and should reflect your own understanding of the subject matter at the time of writing. Any computer code that is used to solve homework problems is considered part of your writeup and should be done individually (you can share ideas, but not code).

No collaboration is allowed on the final exam.

Course Text and References

There is no course textbook, but the slides from the prior year's course serve as a reference for much of the material in the course:

• [Pac23] L. Pachter, Caltech BI/BE/CSS 183: Introduction to Computational Biology and Bioinformatics, Winter 2023.

The following additional references may also be useful:

• TBD
• TBD

Note: the only sources listed here are those that allow free access to online versions. Additional textbooks that are not freely available can be obtained from the library.