The development of omics technologies provides us with an unprecedented opportunity to understand cellular function, and higher levels of organization. In this class, we will focus on big data being generated in biology, and the techniques used to analyze the data. While the course should be self-contained, we assume some mathematical maturity.

Instructors: Vineet Bafna/Sheng Zhong

TA: Keegan Flanagan, Ethan Armand (earmand@ucsd.edu)

Class lectures: TR 5:00 pm -6:20 pm (WLH 2207)

Lecture podcasts will usually be available, but sometimes the technology fails. Please come to class and participate.
We will use Canvas for quick access to course material and other resources
Notes on the lecture material are available here.
All announcements and discussions on Piazza.
Assignments will be handed out on Canvas and submitted using Gradescope (Code: 6KJZK3)
Academic Integrity (Please read)

Office hours:

Vineet Bafna office hours (Tuesday 1:30-2:30p in FAH4303 via zoom. If you plan to join the OH, I will appreciate a note via piazza)
Keegan Flanagan office hours (Thurs 3p-4p via zoom )
Ethan Armand office hours (Wednesday 1p-2p in CMME 2044 in person and on zoom )
Dr. Sheng Zhong office hours (TBD)

Assignment	Due Date	Data and Resources	Comment
Assignment 1	Extended to 4/14 11:00 pm PT	Hi-C data file (informative reads only)
Assignment 2	4/25 11:00 pm PT	Data file for BF (in fastq format) queries	Please write your own code for bloom filters, and submit your code.
Assignment 3	5/9 11:00 pm PT	CAH Bovine protein Mass spec (mgf)	(mgf format for MS data)

Midterm 2024	5/13 (in class)	Prev Midterm exam
Project

Date	Topic	Lecture slides	Optional reading
Week 1		Course Logistics (ppt)
Week 1,2 4/1 4/3	Topic 1: Sources of molecular data and data pre-processing Next-gen sequencing 10X Hi-C RNA profiling Metagenomic sequencing ChIP-Seq Protein mass spectrometry	Slides: Sources of OMICS Data (ppt) pdf (Sequencing) Mass Spectrometry (ppt) pdf (MS)
Week 2 4/8 4/10	Topic 2: NGS data pre-processing, and feature extraction. Abstract notion of database filtering Pigeonhole principle and db filtering Building protein and transcript expression matrices	2.1.Alignment basics (pptx) 2.3. Database filtering (pptx)	'Tuxedo 2' Protocol FM index Wiki Aho Corasick Trie Wiki
Week 3 4/15 4/17	Topic 2. Bloom filters Sketching Locality Sensitive Hashing(tentative)	Bloom Filter video Alignment Free, Assembly Free methods	Sequence Bloom Trees Sketching in Mash
Week 4 4/22 4/24	Topic 3. Genome segmentation using Hidden Markov Models	HMMs HMM slides (pdf)	Rabiner chromHMM
Week 5 4/29 5/1	Topic 4. Mass spectrometry data preprocessing Peptide identification (de novo and database search) Quantification	ms (pptx) (pdf of slides)	Extra Reading MaxQuant Small molecule MS of human skin Database Search using MS-GF+
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Week 6 5/6	Topic 5. Manipulating high dimensional spaces Projections Eigenvalues Hyperplane	pptx pdf of slides	Linear discrim. analysis Perceptron Lin. Programming
Week 6 5/8	Classification, regression and scaling Perceptron algorithm Fischer linear discriminant analysis Large margin separation SVM Neural network basics	Classification pptx	Linear discrim. analysis Perceptron Lin. Programming
Week 7A 5/13	Midterm		Submit using gradescope
Week 7B	Classification of recurrence vs. non-recurrence breast cancer patients based on extracellular RNA	Overview of the 2nd half Lecture 0 - Machine Learning Lecture 1 slides	PNAS 2019
Week 8	Classification of Alzheimer’s vs. control subjects. Introduction to longitudinal data: linear model, mixed model, classification.	Lecture 2 slides Lecture 3 slides	Curr Biol 2020
Week 9	Single-cell ATAC-seq in Alzheimer’s brain Single-cell sequencing and time-course clustering	Project Discussion Signup Lecture 4 slides Lecture 5 slides	Genome Res 2014 PNAS 2014
Week 10	Project presentations	Sign-up sheet	Two-people teams are encouraged.