Arrival: 19 July 2017
Departure: 31 July 2017
Institutional home pages
I have done an extensive update of the lecture slides on codon models for 2016 & 2017. The update includes greater coverage of the mechanistic process of codon evolution (via the MutSel framework). Because there is now less detail about fitting codon models to real data, I have included links to the 2015 slides below; these slides provide more practical information about the powers and pitfalls of inference under codon models.
Note: Updates to lecture slides will be posted for 2018 (those slides will be similar to those for 2017).
2017 Lecture slides, Parts 1 & 2: PDF file1 (2 slides per page)
2017 Lecture slides, Part 3: PDF file2 (2 slides per page)
2017 Lecture slides, Part 4: PDF file3 (2 slides per page)
2016 Lecture slides, Part 1: PDF file1 (1 slide per page)
2016 Lecture slides, Part 2: PDF file2 (1 slide per page)
2015 Lecture slides, Part 1: PDF file1 (2 slides per page)
2015 Lecture slides, Part 2: PDF file2 (2 slides per page)
Key papers related to the Lectures
Phenomenological load (PL) and biological conclusions under codon models:
(Jones C.T., Youssef N., Susko E., Bielawski J.P., 2018. Phenomenological Load on Model Parameters Can Lead to False Biological Conclusions. Mol Biol Evol. 35(6):1473-1488.)
Review of major inference challenges under codon models:
(Jones C.T., Susko E., Bielawski J.P., 2018. Looking for Darwin in genomic sequences: validity and success depends on the relationship between model and data. In Evolutionary Genomics: Statistical and Computational Methods. Maria Anisimova (ed.) 2nd edition (In Press), Human press.)
Positive selection, purifying selection, shifting balance & fitness landscapes:
(Jones, C., Youssef, N., Susko, E. and Bielawski, J., 2017. Shifting balance on a static mutation-selection landscape: a novel scenario of positive selection. Molecular Biology and Evolution, 34(2):391-407.)
Smoothed Bootstrap Aggregation (SBA) for assessing and correcting paramater estimate uncertainty in codon models:
(Mingrone, J., Susko, E. and Bielawski, J., 2016. Smoothed bootstrap aggregation for assessing selection pressure at amino acid sites. Molecular Biology and Evolution, 33(11):2976-2989.)
Protocols, experimental design, and best practices for inference under complex codon models:
(Bielawski, J.P., Baker, J.L. and Mingrone, J., 2016. Inference of episodic changes in natural selection acting on protein coding sequences via CODEML. Current Protocols in Bioinformatics, pp.6-15.)
PAML lab Materials
The lab exercises (PAML demo) have been published to a small website (link below). The site contains some additional resources. Please note that presentations may change a little prior to the lab. I will post modified PDFs as required.
PAML demo: Link to the new website
PAML demo resources: Link
PAML demo slides: PDF file (2 slides per page)
Lab materials can also be downloaded from a repository: Bitbucket repository
Supplemental PAML lab notes
PAML demo: Link to the old website
If you have some experience with codon models, and want to try out a tutorial for more advanced materials then use the link below to download an archive for a complete different set of PAML activities. This tutorial focuses on detecting episodic protein evolution via Branch-Site Model A. The tutorial also includes activities about (i) detecting MLE instabilities, (ii) carrying out robustness analyses, and (iii) use of smoothed bootstrap aggregation (SBA). The protocols for each activity are presented in Protocols in Bioinformatics UNIT 6.15. The included PDF file for UNIT 6.16 also presents recommendations for "best practices" when carrying out a large-scale evolutionary survey for episodic adaptive evolution by using PAML. The files required for this "alternative lab" are available via Bitbucket repository. The repository link is given below.
Advanced PAML demo: Bitbucket repository
"Best practices" in large-scale evolutionary surveys
Large-scale evolutionary surveys are now commonplace. But with the use of progressively more complex codon models, these surveys are fraught with perils. Complex models are more prone to statistical problems such as MLE irregularities, and some can be quite sensitive to model misspecification. UNIT 6.16 (see above) provides some recommended "best practices" for a 2-phase approach to quality control and robustness in evolutionary surveys. We have applied these to a large scale survey for functional divergence in nuclear receptors during homing evolution, and we used experimental approaches to investigate hypotheses about the role of a particular nuclear receptor (NR2C1) as a key modulator of developmental pluripotnetiality during hominid evolution. The paper that illustrates the power of such an evolutionary surgery, and the importance of an experimental design having explicit protocols for "best practices", is given below.
Example large-scale survey: PDF reprint