Schedule

I will update this page before every class with the links to in-class materials and assignments. To view the planned schedule of course activities, view the class calendar using the button below.

Class Calendar

Week 1 (Jan 21, 23)

• Lecture 1: Introduction to Molecular Phylogenetics [slides]
  • Assignment 1 (due 1/23)
• Lecture 2: Trees and characters [notes]
  • Assignment 1 is due.
  • Reading: Allman and Rhodes (2016). Chapter 2: Combinatorics of Trees I.
  • Discussion: Baum et al. (2005). The Tree-Thinking Challenge. Science 310: 979-980.

Week 2 (Jan 28, 30)

• Lecture 3: Homology and Sequence Alignment [notes]
  • Reading: Chatzou et al. 2016. Multiple sequence alignment modeling: methods and applications Briefings in Bioinformatics, 17(6), 2016, 1009–1023.
  • Discussion: Gabaldón and Koonin (2013). Functional and evolutionary implications of gene orthology. Nature Reviews Genetics volume 14, pages 360–366.
    
• Computer Lab 1: Introduction to Unix/Multiple Sequence Alignment (MSA).
  • Complete all prerequisites before coming to the class!
  • Post any question you may have on Slack.
  • Assignment 2 (due 02/06)

Week 3 (Feb 4, Feb 6)

• Lecture 4: Parsimony and cladistics. Optimality criteria and character optimization.
  • Reading: Allman and Rhodes (2016). Chapter 3: Parsimony. [notes]
• Lecture 5: Searching tree space. Measures of character fit. Assessing clade support.
  • Reading: Allman and Rhodes (2016). Chapter 9: Tree Space. [notes]
  • Discussion: Baron et al. (2017). A new hypothesis of dinosaur relationships and early dinosaur evolution. Nature 543: 501-506. See also News & Views, Langer et al. communication, and Baron et al. reply.

Week 4 (Feb 11, 13)

• Computer Lab 2: Parsimony analysis.
  • You may want to install Mafft, PAUP*, and TNT on your computer [links]. However, we will use HPC-class cluster for the excercises.
  • Assignment 3 (due 02/18)
• Lecture 6: Distance matrix methods. Clustering algorithms. [notes]
  • Reading: Allman and Rhodes (2016). Chapter 5: Distance Methods.

Week 5 (Feb 18, 20)

• Lecture 7: NJ algorithm; Model-based distances. [notes]
  • Reading: (Optional!) Allman and Rhodes (2016). Chapter 6: Probabilistic Models of DNA Mutations.
  • Discussion: Naxerova et al. (2017). Origins of lymphatic and distant metastases in human colorectal cancer. Science 357, 55-60.
• Computer Lab 3: Distance analysis with PAUP, PHYLIP, and FastME.
  • Assignment 4 (due 02/27)

Week 6 (Feb 25, 27)

• Lecture 8: Introduction to Maximum Likelihood. [notes]
  • Reading: Allman and Rhodes (2016). Chapter 8: Maximum Likelihood.
• Lecture 9: Constructing Phylogenetic Trees Using Maximum Likelihood. [notes]
  • Discussion: Basem Al-Shayeb et al. (2020). Clades of huge phages from across Earth’s ecosystems. Nature 578, 425–431. If you want to brush up your knowledge on bacteriophages, here is a nice review from the same issue of Nature.

Week 7 (Mar 4, Mar 6)

• Computer Lab 4: Likelihood analysis in RAxML and IQ-Tree.
  • Assignment 5 (due 03/11)
• Lecture 10: Bayes’ theorem and Bayesian methods in phylogenetics. [notes]
  • Reading: Allman and Rhodes (2016). Chapter 12: Bayesian Inference.

Week 8 (Mar 11, 13)

• Computer Lab 5: Bayesian analysis with MrBayes.
  • Assignment 6 (due 03/27)[dataset]
• Lecture 11: Applications of Bayesian methods. [notes]
  • Discussion: Williams et al., 2020. Microbial predators form a new supergroup of eukaryotes Nature, 612:714-719.

Spring Break! (Mar 18, 20)

• Don’t forget about your final project outline! (due 3/27)

Week 9 (Mar 25, 27)

• Lecture 12: Model selection and model averaging in Likelihood and Bayesian methods [notes].
  • Reading: Posada & Buckley (2004). Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests. Systematic Biology 53: 793–808.
  • Discussion: Williamson, K., Eme, L., Baños, H. et al. (2025). A robustly rooted tree of eukaryotes reveals their excavate ancestry. Nature
• Lecture 13: * Midterm exam review and final project discussion
  • Be ready to present your final project outline. Include hypotheses, data, and proposed methods for the project. In addition, create a GitHub repository for the final project and send me the link.

❗ Midterm exam ❗

• Exam will open on Apr 1, after the class. Will cover weeks 1-8
• Exam will close on Apr 8, before the class.
• You will have one 3hr window to complete the exam.

Week 10 (Apr 1, 3)

• Lecture 14: Neutral and adaptive protein evolution [notes].
  • Reading: Sackton (2020). Studying Natural Selection in the Era of Ubiquitous Genomes. Trends in Genetics 36: 792-803.
  • Discussion: Romiguier et al. (2022). Ant phylogenomics reveals a natural selection hotspot preceding the origin of complex eusociality. Current Biology 32: 2942-2947.e4.

• Computer Lab 6: Hypotheses testing with PAML.
  • Reading: Álvarez-Carretero et al. (2023). Beginner’s Guide on the Use of PAML to Detect Positive Selection. Molecular Biology and Evolution 40: msad041.

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