Botany/PlantPath 563

Phylogenetic Analysis of Molecular Data (UW-Madison)

A course in the theory and practice of phylogenetic inference from DNA sequence data. Students will learn all the necessary components of state-of-the-art phylogenomic analyses and apply the knowledge to the data analyses of their own organisms.

  • Spring 2024: Tuesday and Thursday 1:00-2:15pm
  • Instructor: Claudia Solis-Lemus, PhD
  • Office hours: Tuesday 2:15-3:15pm, or by appointment

Learning outcomes

By the end of the course, you will be able to

  1. Explain in details all the steps in the pipeline for phylogenetic inference and how different data and model choices affect the inference outcomes
  2. Plan and produce reproducible scripts with the analysis of your own biological data
  3. Justify the data and model choices in your own data analysis
  4. Interpret the results of the most widely used phylogenetic methods in biological terms
  5. Orally present the results of your own phylogenomic data analyses based on the best scientific and reproducibility practices

Textbooks and references

  • Phylogenetics in the Genomic Era (open access book) by Celine Scornavacca, Frederic Delsuc and Nicolas Galtier (denoted HAL in the schedule)
  • Tree thinking: an introduction to phylogenetic biology by David Baum and Stacey Smith (optional: denoted Baum in the schedule)
  • The Phylogenetic Handbook by Philippe Lemey, Marco Salemi and Anne-Mieke Vandamme (optional: denoted HB in the schedule)
  • The full list of papers used in this class can be found in this link

Schedule 2024

Session Topic Pre-class work At the end of the session Lecture notes Homework
01/23 Introduction   You will know what will be the structure of the class, the learning outcomes and the grading lecture1 Go over ready-for-class checklist
01/25 Reproducibility crash course (Part 1) Review shell resources and do canvas quiz You will prioritize reproducibility and good computing practices throughout the semester (and beyond) lecture3 Learn@Home: Why learn phylogenomics? (due 01/30)
01/30 Reproducibility crash course (Part 2) Have git installed     Reproducibility HW (due 02/06)
01/30 HW due: Learn@Home: Why learn phylogenomics?        
02/01 Alignment (Part 1)   You will be able to explain the most widely used algorithms for multiple sequence alignment lecture5 Learn@Home: Sequencing (due 02/08)
02/06 Alignment (Part 2)     lecture5-2 Needleman-Wunsch HW and canvas quiz (due 02/13)
02/06 HW due: Reproducibility        
02/08 Alignment (paper discussion) One paper assigned per student: 1) ClustalW, 2) MUSCLE, 3) T-Coffee   lecture5-3  
02/08 HW due: Learn@Home: Sequencing        
02/13 Alignment (computer lab)     lecture5-4 Alignment HW (due 02/20)
02/13 HW due: Needleman-Wunsch HW        
02/15 Overview of phylogenetic inference   You will be able to explain the overall methodology of phylogenetic inference as well as the main weaknesses lecture7 Learn@Home: Orthology and Filtering (due 02/22)
02/20 Distance and parsimony methods (Part 1) Optional readings: HB Ch 5-6, Baum Ch 7-8 You will be able to explain both algorithms to reconstruct trees: 1) based on distances and 2) based on parsimony lecture8  
02/20 HW due: Alignment HW        
02/22 Distance and parsimony methods (Part 2)     lecture8-2  
02/22 HW due: Learn@Home: Orthology and Filtering        
02/27 Distance and parsimony methods (computer lab) Install R   lecture8-3 Distance and Parsimony HW (due 03/05)
02/29 Models of evolution (Part 1) HAL 1.1 and canvas quiz You will be able to explain the main characteristics and assumptions of the substitution models lecture9  
03/05 Models of evolution (Part 2)        
03/05 HW due: Distance and Parsimony HW        
03/07 Maximum likelihood HAL 1.2 and canvas quiz You will be able to explain the main steps in maximum likelihood inference and the strength/weaknesses of the approach lecture10  
03/12 Maximum likelihood (paper discussion) Two papers assigned per student: 1) IQ-Tree papers: one, two; 2) RAxML papers: one, two   lecture10-2 Learn@Home: Model Selection (due 03/14)
03/14 Maximum likelihood (computer lab)     lecture10-3 Maximum Likelihood HW (due 03/21)
03/14 HW due: Learn@Home: Model Selection        
03/19 Bayesian inference (Part 1) HAL 1.4 and canvas quiz You will be able to explain the main components of Bayesian inference and their effect on the inference performance lecture12  
03/21 Bayesian inference (Part 2) Read Nascimento et al, 2017 and quiz     Read YangRannala1997
03/21 HW due: Maximum Likelihood HW        
03/26 Spring break        
03/28 Spring break        
04/02 Bayesian inference (paper discussion) Read depending on your canvas group: 1) MrBayes papers: one, two; 2) RevBayes   lecture12-2  
04/04 Bayesian inference (computer lab)     lecture12-3 Bayesian Inference HW (due 04/11)
04/09 The coalescent HAL 3.1 and quiz, HAL 3.3 and quiz You will be able to explain the coalescent model for species trees and networks lecture14  
04/11 The coalescent (computer lab) Read ASTRAL and BUCKy   lecture14-2.md Coalescent HW (due 04/23)
04/11 HW due: Bayesian Inference HW        
04/16 The coalescent (networks) SNaQ chapter and quiz   lecture14-3  
04/18 Co-estimation methods Optional reading: HB 18 You will be able to explain the main components of co-estimation methods and follow the BEAST tutorial lecture15  
04/23 Co-estimation methods (computer lab) Optional: Read BEAST papers: one, two   lecture15-2  
04/23 HW due: Coalescent HW        
04/25 What else is out there? (Final project Q&A) Read Jermiin2020 again You will hear a brief overview of topics not covered in this class and will have access to resources to learn more lecture16  
04/30 Project presentations        
05/02 Project presentations        
05/06 Final project+reproducible script due        

More details

See list of topics, grading and academic policies in the syllabus

Copyright Solis-Lemus lab © 2022. Distributed by an MIT license.