Computational Biology Practicum – Spring 2017

Botany 5555

Instructor: Alex Buerkle
Class meetings: Enzi STEM Building (north of Lewis Street) Room 155, 1:20–2:35 p.m. on Tuesday and Thursday

During the semester, dynamic content for the course will be available on the Course page on WyoCourses (restricted to class participants). All course materials will be distributed electronically via WyoCourses.

Course syllabus (spring 2017, pdf)

Most subdisciplines in modern biology involve the analysis of large amounts of data, inference based on probabilistic models, or both. This course builds on students' previous experience in computational biology and extends it into the context of applied, computational analysis of biological data. In particular, the analyses will be performed for biologist clients, who will provide real data from on-going projects that require computational statistical analysis. The clients will lay out the scientific context and motivation, and be the subdisciplinary expert in biology. Teams of students will work together, with consultation and direction from the instructor, to answer the scientific questions and perform the analyses for the client. The research products will be delivered, evaluated and refined at intermediate stages, through written and in-person presentations with the client. Likewise, final research products will be delivered to the client and will utilize best practices in report generation and reproducible research. The delivery of analyses to clients is a valuable experience for data science careers in academics, government or business.

2017 clients

  1. Bob Hall, University of Wyoming
    Project: novel estimation methods for river metabolism with variable flow rates
  2. Tom Parchman, University of Nevada–Reno
    Project: comparison of methods for de novo assembly of reduced representation DNA sequencing libraries

We will have a few main goals in this class: 1) to gain expertise and become proficient in the application of probability, mathematics and computational methods in biology, 2) to understand philosophies and conceptual frameworks underlying analytical methods, and 3) to develop skills in teamwork and reporting of scientific analysis.