Scalable Methods for Post-Processing, Visualizing, and Analyzing Phylogenetic Placements

[Erick]

Hello everyone--

The Stamatakis lab has implemented a guppy-like tool, which is described here:

https://www.biorxiv.org/content/early/2018/06/14/346353

The exponential decrease in molecular sequencing cost generates unprecedented amounts of data. Hence, scalable methods to analyze these data are required. Phylogenetic (or Evolutionary) Placement methods identify the evolutionary provenance of anonymous sequences with respect to a given reference phylogeny. This increasingly popular method is deployed for scrutinizing metagenomic samples from environments such as water, soil, or the human gut. Here, we present novel and, more importantly, highly scalable methods for analyzing phylogenetic placements of metagenomic samples. More specifically, we introduce methods for visualizing differences between samples and their correlation with associated meta-data on the reference phylogeny, as well as for clustering similar samples using a variant of the k-means method. To demonstrate the scalability and utility of our methods, as well as to provide exemplary interpretations of our methods, we applied them to 3 publicly available datasets comprising 9782 samples with a total of approximately 168 million sequences. The results indicate that new biological insights can be attained via our methods.

As I mentioned before, we're phasing out our tools, so I suggest checking this tool out, along with epa-ng.

Thanks,

Erick