PAML 5: A friendly and powerful bioinformatics resource for phylogenomics

PAML (for Phylogenetic Analysis by Maximum Likelihood) is a bioinformatics software tool widely used in the fields of molecular evolution, molecular phylogenetics, virology, biochemistry, and genomics. It is currently attracting over 1000 citations per year (with a total of >16K citations since its first release in 1993), and has a well-established user base. One of the major strengths of the package is its rich collection of sophisticated models for DNA and protein sequence evolution, which are useful in maximum likelihood and Bayesian methods in phylogenetics. The package can be used to compare different evolutionary trees, to infer adaptive molecular evolution affecting protein-coding genes, to reconstruct sequences in extinct ancestral species, etc. Despite being a widely used bioinformatics tool, PAML has a poor user interface and a steep learning curve. It is not parallelized, and is computationally inefficient when applied to large datasets. These issues have hindered its widespread adoption by new users and mean that existing users have to endure the computational burden.

In this project, we propose to redesign and reimplement the key algorithms in the PAML programs, parallelize the code, and also develop a R interface. We will expand and improve its functionality by developing new mutation-selection models of codon evolution, which will lead to more accurate ancestral sequence reconstruction and more robust detection of genes under positive selection. Those improvements will greatly improve the usability and computational performance of the program package, making it an extremely valuable bioinformatics resource for the biosciences research community.

This project is low-risk and high-impact. HPC technology, Pthreads and MPI have been around for many years. In phylogenetics, all the major likelihood and Bayesian programs have already been successfully parallelized, including PhyML, RAxML, MrBayes/RevBayes, and BEAST. Indeed PAML is uniquely bad in this...