You can download the lectures here. We will try to upload lectures prior to their corresponding classes. Majority of slides used in this class were originally created by Prof. Robert Beiko and modified (to greater and lesser degrees) by myself.

  • 01 Course Overview & Life at Resolution
    tl;dr: Overview of the course, motivational plagues, and biology background from DNA to Ecosystems
    [slides] [recording]
  • 02 Central Dogma & Pathways
    tl;dr: Foundational material on biochemical pathways and how central dogma determines how biological data is copied and interpreted by cells
    [slides] [recording]
  • 03 Molecular Evolution
    tl;dr: Foundational material on molecular evolution
    [slides] [recording]
  • 04 Sequence Representations
    tl;dr: Strings and structures for encoding biological sequence data for computation
    [slides] [recording]
  • 05 Sequence Alignment - Definitions and Scoring
    tl;dr: How we define and score sequence alignments
    [slides] [recording]
  • 06 Optimal Alignment - Dynamic Programming
    tl;dr: How dynamic programming approaches can be used to generate optimal alignments
    [slides] [recording]
  • 07 Fast Sequence Searches: BLAST and friends
    tl;dr: Overview of the BLAST algorithm and related approaches
    [slides] [recording]
  • 08 Algorithms and Data Structures for Read Mapping
    tl;dr: An overview of algorithms for mapping reads to a genome (suffix trees, suffix arrays, and burrows-wheeler transform
    [slides] [recording]
  • 09 Efficient MSA and HMMs
    tl;dr: An overview of algorithms for efficient multiple sequence alignment and introduction to Hidden Markov Models
    [slides] [recording]
  • 10 Genomic Assembly - Overlap Layout Consensus
    tl;dr: OLC algorithm for recovering genomes from sequencing data
    [slides] [recording]
  • 11 de Bruijn Graphs and Metagenomic Assembly
    tl;dr: dBG methods and assembly algorithms for assembling mixed sequencing data from multiple genomes at once
    [slides] [recording]
  • 12 Introduction to Phylogenetics
    tl;dr: Basic concepts of phylogenetics and parsimony algorithms.
    [slides] [recording]
  • 13 Distance and Maximum Likelihood Phylogenies
    tl;dr: Using maximum likelihood methods to infer evolutionary trees
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  • 14 Bayesian Phylogenetic and Statitical Support
    tl;dr: Using Bayesian methods to infer evolutionary trees and inferring the statistical support of different trees
    [slides] [recording]
  • 15 Outbreak Analysis
    tl;dr: Combining phylogenetic methods to respond to infectious disease outbreaks
    [slides] [recording]
  • 16 Phylodynamics
    tl;dr: Building on top of phylogenetics to investigate the evolution and ecology of pathogens
    [slides] [recording]
  • 17 Phylogenetic Networks & Reconciling Trees
    tl;dr: Lecture on phylogenetic inference of LGT
    [slides] [recording]
  • 18 Training ML Models
    tl;dr: Basics of classification and how we train and choose supervised learning models
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  • 19 ML Features
    tl;dr: Feature selection and extraction for biological ML
    [slides] [recording]
  • 20 Machine Learning Examples
    tl;dr: Whirlwind tour of some example uses
    [slides] [recording]