CS 618
This page contains the course syllabus and plan for CS 618 Computational Biology (run also as CS 459 Topics in CS for undergrads) for the summer of 2024. The basic plan is to look at a number of projects that I have worked on in the past, and to look at key tools and algorithms used in computational biology and bioinformatics.
The top 3 goals for the course - (1) being able to use programming, tools, etc. to work on biology-related projects and data, (2) understanding some of the key algorithms, statistics, etc. used in this area, and (3) understanding as much of the biology as we can, in particular related to where the data comes from, what it means, etc. And tying all of these together will be working on some projects.
Contents
General Information
Course website' - https://cs.indstate.edu/wiki/index.php/CS_618
Your Instructor
Jeff Kinne, jkinne@cs.indstate.edu
Office: Root Hall A-142 and in Microsoft Teams, phone 812-237-2126
Instructor Office Hours: MTWR 11am-noon
Meeting: https://cs.indstate.edu/jkinne-meeting
Lecture, Exam
Lecture: Course is being run asynchronously, so the regular lecture hour time is being used as office hours. If you want to meet you can schedule a meeting with the meeting link, or join the zoom meeting that is in the course (and if I am available I will join as well)
Mid-term exam: TBA
Final exam: TBA
Prerequisites - no prerequisite.
CRN numbers - 30693 for the 001 face to face section, 30695 for the 301 online section
Required text We will use all online sources. This section will be updated as we go through them.
Class notes - Notes during class will mostly be kept in the documents in this OneDrive folder. Note that you will need to authenticate with your ISU account to view the notebook.
Programming/Tools
R programming, including commonly used packages.
Python programming, including commonly used packages.
Other programming - javascript/node, bash.
Software/tools - BLAST, NCBI.
Algorithms/Statistics
Statistics
Clustering techniques
Sequence alignment algorithms
Biology
Central dogma of genetics
Biological data - different types of assays, etc. - how the data is produced, what the data looks like, etc.
Projects
Gene expression - determining key genes from gene expression datasets. Project is in R, uses Shiny, Datatables, ShinyProxy, Docker. Poster - https://cs.indstate.edu/info/posters/bd4isu2022-bartlett.pdf
Protein topology prediction - finding potential transmembrane proteins in genomes. Project is in Python, uses Javascript, NCBI, BLAST. Poster - https://cs.indstate.edu/info/posters/bd4isu2022-hoffman.pdf
Transcription factors - finding mutations to disable a transcription factor while still preserving others. Project is in Python, R, and/or C. Poster - https://cs.indstate.edu/info/posters/bd4isu2020-bennett.pdf
Gene expression - determining key genes in a particular dataset from fish. Poster - https://cs.indstate.edu/info/posters/bd4isu2021-gosnell.pdf
Mass spectrometry data - keeping a database of mass spec data and searching through databases for new samples. Potential new project.
Genome sequencing - doing whole genome sequencing for species that have not yet had this done. New project with one of the students in the course.
Sequencing - different sequencing technologies (RNA seq, CHIP seq, single cell RNA seq, etc.), pros/cons/costs/what-used-for/etc., for a study looking cancer in a model organism.
Other requested topics...
Drug discovery/modeling - modeling/simulating drug interactions with the body and the drug discovery process.
Resources
Watch list
Watch starting from the bottom.
- single cell seq
- ATAC seq
- high throughput sequencing (StatQuest)
- genetics (and continue watching subsequent videos up through mutations
- cell
Data files
- GSE85331 Liu et al - see file GSE85331_all.gene.FPKM.output.replicates.txt.gz at the bottom.
Programs to install
- 7-zip - for extracting zip files, if your OS cannot unzip them already (e.g., Windows 10).
Course Description and Content
Course Description
The catalog description for this course is: "An introduction to computational biology. Topics may include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. A focus is placed on the computational cost of solving problems in terms of CPU time, memory, and disk space. Study of the core algorithms used to solve problems."
Course Outline
Learning Outcomes
