Here are my notes of the article about ClonalFrameML, a program that detects recombined regions in a multi-sequence alignment, infers phylogenetic relationships when correcting for recombination, reconstructs ancestral state, and imputes SNPs under a maximum-likelihood (ML) framework.
In this post, I compile my notes of the course Metagenomics applied to surveillance of pathogens and antimicrobial resistance. This three-week course is offered by the Technical University of Denmark and is freely accessible at Coursera. As a graduate researcher working on antimicrobial resistance (AMR) in bacterial populations, I have read countless pieces of literature about bacterial population genomics, surveillance and metagenomics in the most recent four years, and I am supposed to be familiar with the content of this course. Nevertheless, the course remains quite helpful to me since it leads me to build a comprehensive knowledge framework of metagenomics from individual concepts. Here, I focus on knowledge that was once unfamiliar or ambiguous to me, and it may be new to some readers as well. More information can be found in course materials on Coursera.
Phylogenetic reconstruction is of crucial importance to elucidate bacterial population structure, epidemiology and evolutionary histories. By far phylogenetic networks and trees are the most common approaches used for studying the evolutionary history of a bacterial population. However, concepts and methodology underlying phylogenetic reconstruction can be challenging to beginners. As such, I share my notes on relevant literature in this post to address these obstacles. In particular, I compare different kinds of phylogenetic networks to show their pros and cons under various conditions.