Lecture 1: Course Overview

Goals for today
  • Introduce course goals, grading policy, and materials.
  • The programming notebook: what is expected?
  • Introduce Bioinformatics as a field of study, and types of biological data.

What is bioinformatics? It’s a good question: the field is very new and it’s hard to find a consistent definition. One definition is the application of algorithms, databases, and statistical methods to address biological questions.

What roles must bioinformatics fill? Where can we go?
  • algorithm development and optimization
  • interface development
  • visualization
  • development of statistical methods (non-parametric statistics, machine learning, ...)
Biology is swamped in data.
  • genome sequences
  • novel organisms (mostly microorganisms)
  • new gene sequences: what do they do?
  • gene expression data
  • molecular structure data
  • microbial community profile data and metagenomes
  • -ome data: genomes, proteomes, transcriptomes, microbiomes, metagenomes, the list goes on and on and on...
What are we going to cover this semester?
  • Very focused introduction to molecular biology (a molecular biology class will be extremely valuable if you want to pursue bioinformatics as a career)
  • Python programming
  • How to find, understand, and evaluate Bioinformatics software to address specific problems (don’t reinvent the wheel!).
  • How to use, integrate, or extend software developed by others.
Where should you be able to go next?
  • Consider the new Bioinformatics track at NAU (currently in development; contact Greg Caporaso for details).
  • Join a research lab: independent study, senior thesis, volunteer for the summer in a lab at NAU, internships.
Homework example
  • Exercise 0: The Setup