Bi/BE/CS 183, Winter 2024
Introduction to Computational Biology and Bioinformatics  
Instructors

Teaching Assistants

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 (306): second term. Prerequisites: Bi 8, CS 2, Ma 3; or BE/Bi 103 a; or instructor's permission. Biology is becoming an increasingly dataintensive 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 RNASeq data sets.
Lecture Schedule
Date  Topic  Reading  Homework 
Week 1 3 Jan 
Course Introduction


HW #1
Out: 3 Jan 
Week 2 8 Jan 
Correlation and regresssion

HW #2
Out: 10 Jan  
Week 3

Dimensionality reduction

HW #3
Out: 17 Jan  
Week 4 22 Jan 
Expectation maximization (EM)

HW #4
Out: 24 Jan
 
Week 5 29 Jan 
Read alignment and modeling counts

HW #5
Out: 31 Jan  
Week 6 5 Feb 
Transformation of nonnormal distributions


HW #6
Out: 7 Feb 
Week 7 12 Feb 
Differential analysis


HW #7
Out: 14 Feb 
Week 8

Hidden Markov models


HW #8
Out: 21 Feb 
Week 9 26 Feb 
Markov processes


HW #9
Out: 28 Feb 
Week 10 4 Mar 
Machine learning


Final (GradeScope)
Out: 8 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). For Colab notebooks, first use the Runtime > Run all command to execute all code cells, then use File > Print to save the notebook and the outputs to a pdf.
 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.