CS618 - Computational Biology

Spring 2022

Class Hours

Lectures are held F, 3:00-4:00 in Zoom . See syllabus for information on joining lectures by Zoom or watching recordings.

Office Hours

Jeff Kinne's office hours: M-F 11-noon, 2-3pm (see syllabus for how office hours will work) in Root Hall A-142 and in Teams.

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."

We will take some time to go through fundamental CS (terminal/linux, programming, program analysis, etc.) and biology (experimental validation, maybe some others) concepts. We then focus on computational biology problems that are parts of research at ISU. We will study different types of biological sequencing and algorithms for dealing with these. We will also survey clustering algorithms and see their application to research. We will use ISU research (the instructor's, and students' research if you would like) as case studies throughout. Other topics may be covered as time allows and depending on interests of the class.

Learning Outcomes

  • Able to use the terminal to run tools, programming languages, and look at / edit files - both on your personal computer and in linux on ISU systems (CS server).
  • Basic understanding of programming languages commonly used in computational biology - what each is used for, ability to compile/run programs in each, able to make small-ish changes to programs given to you.
  • Can explain fundamental concepts in computer science and apply these to problems/algorithms covered in the course - running time, memory usage, asymptotic analysis, multi-CPU and GPU programs, software testing.
  • Can explain fundamental concepts in biology and apply these to problems/algorithms covered - experimental errors, experiment design, basic statistics, central dogma of genetics, sequencing technologies and data formats.
  • Can explain and use a variety of classification and clustering algorithms (strengths, weaknesses, uses) as well as key concepts in machine learning.
  • Can run bioinformatics tools to process biological data, perform analysis, and interpret the results.

Recommended text

Free online sources