Description

02-510 / 02-710 - Computational Genomics

Course relevance: Undergraduate students in the computational biology major and minor. Graduate students in computational biology and graduate students who have an interest in algorithmic techniques in computational genomics.

Key topics:
- Sequence alignment
- High-throughput sequencing data analysis
- Analysis of gene expression data
- Epigenetics and genome organization
- Single-cell data analysis
- Complex biological networks
- Application to specific biological processes and diseases

Background knowledge: machine learning methods, probabilistic modeling, programming, algorithms and data structures

Course goals/objectives: dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as single-cell systems biology, is demanding new methodologies that can confront quantitative issues of substantial computational sophistication. In this course, we will discuss both classical approaches and the latest methodological advances in the context of genomics and systems biology. From the computational side, this course covers a wide range of modern data analysis and machine learning methodologies for computational problems in molecular biology.

General Information

Time & location
Time: Monday & Wednesday: 02:00PM - 03:20PM
Location: MI 348
Schedule
1/17 Introduction to genomics
1/22 Mathematical preliminaries
1/24 Sequence alignment
1/29 Read mapping
1/31 BWT, FM-index, and advanced string data structures
2/05 Sketching for genomic analysis
2/07 Variant calling
2/12 Normalization
2/14 DE analysis
2/19 Clustering
2/21 Classification
2/26 Protein-DNA interactions
2/28 Exam 1
3/11 Chromatin states and HMM
3/13 3D genome analysis - basics
3/18 3D genome analysis - advanced topics
3/20 Haplotyping, imputation
3/25 Metagenomics
3/27 Linkage analysis, GWAS
4/01 Dimension reduction
4/03 Genome data formats and compression
4/08 Cancer genomics
4/10 Exam 2
4/15 Biomedical and genomic privacy
4/17 Network biology
4/22 LLM for genomics
4/24 Poster session
Assessment Structure
4 problem sets (50%)
2 midterm exams (30%)
1 individual final project (20%)
Midterm exam dates
The midterm exams will be in-class, on Wednesday, Feb 28, and Wednesday, April 10.
Project
Must be relevant to the class topics. You will be individually responsible for your project, though collaboration (with attribution) will be encouraged. A few possible project topics will be provided later in the class, though we encourage proposing other topics as well. Instruction on presentations for projects and on the write-up will be provided later in the class.
Late day policy
All students can have 3 late days in total for the whole semester. You can use the late days on any of the problem sets. You can use 1, 2, or all 3 for a single problem set. No partial days allowed. So for example, if you submit less than 24 hours after the deadline, then we will count it as a full day. If you have run out of late days, there will be a 10% penalty for every day your assignment is late, but you may not turn in any homework assignment more than 3 days late (whether you decide to use one of your free late days or decide to take a penalty).

Late days are only applied to problem sets. You cannot use any of your late days for the project. The project must be turned in on time, and it will not be accepted late (i.e. you will receive a 0 for the project if you do not turn it in on time).

Announcements

Announcements are not public for this course.
Staff Office Hours
NameOffice Hours
Jian Ma
When?
Where?
Yun William Yu
When?
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Wenduo Cheng
When?
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Xinyue Lu
When?
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Filipp Nikitin
When?
Where?

General Resources