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CBB 430 Applied Bioinformatics and Computational Biology

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Winter 2025

Jan 7, 2025

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Instructors

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Charles Min

Nettles Lab

Natalie Turner Yates Lab

Ben Calverley

Balch Lab

Andrew Su

ISCB Dept

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Course Philosophy

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Imagine this as all of bioinformatics

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Imagine this as all of bioinformatics

  • Provide a broad overview of all topics in bioinformatics?

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Imagine this as all of bioinformatics

  • Provide a broad overview of all topics in bioinformatics?
  • Cover a selection of bioinformatics topics in great depth?

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Imagine this as all of bioinformatics

  • Provide a broad overview of all topics in bioinformatics?
  • Cover a selection of bioinformatics topics in great depth?
  • Provide guidance and confidence in your ability to manage and analyze your own data

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YOU TODAY

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ABCB Course Structure and Grading

  • Three 3-week modules (60%)
    • Module 1: Proteomics
    • Module 2: Phenotype-genotype associations
    • Module 3: Molecular dynamics
  • Capstone project (20%)
    • 2-person teams
    • Reproduce a figure from a paper starting with raw data
    • Grading based on written code and oral presentation (10 mins)
  • Bioconductor presentation (20%)
    • Individually select and present a bioconductor package (5 mins)

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Pass/fail: pass is 70% or higher

Audit - Homework will be graded if submitted, time permitting

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More details to come

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Tech stack

  • R (version 4.4.2)
  • RStudio
  • R Notebooks and/or R Markdown

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Slack

  • We will use the #abcb-2025 channel in the "Su Wu Lab" slack organization
  • Reduce chat clutter. If you see a topic/question similar to yours, reply to it as a thread
  • Write your questions/replies as multi-line posts (use shift-return to add newline) vs multiple individual posts

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Course pacing

  • This course will prioritize learning how to approach bioinformatics problems over sticking to a specific schedule and topic list
  • Wide distribution in prior experience
    • Advanced students should seek out opportunities to help others and to explore advanced topics

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Academic Integrity

  • Reasonable
    • Discussing assignments and course material
    • Helping a classmate find a bug in their code
    • Showing your program to a classmate to help you find a problem in your code
    • Using online resources to solve assignments, so long as they're not solutions to the assigned problems
    • Whiteboarding solutions to assignments with others using diagrams or pseudocode but not actual code�
  • Unreasonable
    • Viewing a classmate's solution in order to solve your assignment
    • Giving your assignment solution to another student
    • Starting with someone else's code and making changes to personalize it
    • Submitting work from a previous course/project as your final project
    • Generative AI tools (ChatGPT, Claude, Llama, etc.), for now…

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Large Language Models (LLM)

Large Language Models are very useful for coding and debugging, but for the moment, don't use it…

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Questions / comments about course structure?

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