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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
- Email: [email protected]
- Website: https://solislemuslab.github.io/
- Office hours: Tuesday 2:15-3:15pm, or by appointment
By the end of the course, you will be able to
- Explain in details all the steps in the pipeline for phylogenetic inference and how different data and model choices affect the inference outcomes
- Plan and produce reproducible scripts with the analysis of your own biological data
- Justify the data and model choices in your own data analysis
- Interpret the results of the most widely used phylogenetic methods in biological terms
- Orally present the results of your own phylogenomic data analyses based on the best scientific and reproducibility practices
- 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
| 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 |
See list of topics, grading and academic policies in the syllabus