GeneRax: A Tool for Species-Tree-Aware Maximum Likelihood-Based Gene Family Tree Inference under Gene Duplication,Transfer, and Loss

Inferring phylogenetic trees for individual homologous gene families is difficult because alignments are often too short, and thus contain insufficient signal, while substitution models inevitably fail to capture the complexity of the evolutionary processes. To overcome these challenges, species-tree-aware methods also leverage information from a putative species tree. However, only few methods are available that implement a full likelihood framework or account for horizontal gene transfers. Furthermore, these methods often require expensive data preprocessing (e.g., computing bootstrap trees) and rely on approximations and heuristics that limit the degree of tree space exploration. Here, we present GeneRax, the first maximum likelihood species-tree-aware phylogenetic inference software. It simultaneously accounts for substitutions at the sequence level as well as gene level events, such as duplication, transfer, and loss relying on established maximum likelihood optimization algorithms. GeneRax can infer rooted phylogenetic trees for multiple gene families, directly from the per-gene sequence alignments and a rooted, yet undated, species tree. We show that compared with competing tools, on simulated data GeneRax infers trees that are the closest to the true tree in 90% of the simulations in terms of relative Robinson–Foulds distance. On empirical data sets, GeneRax is the fastest among all tested methods when starting from aligned sequences, and it infers trees with the highest likelihood score, based on our model. GeneRax completed tree inferences and reconciliations for 1,099 Cyanobacteria families in 8 min on 512 CPU cores. Thus, its parallelization scheme enables large-scale analyses. GeneRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax (last accessed June 17, 2020).       

AxPcoords &; parallel AxParafit: statistical co-phylogenetic analyses on thousands of taxa

Background  

Current tools for Co-phylogenetic analyses are not able to cope with the continuous accumulation of phylogenetic data. The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times. The Parafit and DistPCoA programs are the by far most compute-intensive components of the Parafit analysis pipeline. We present AxParafit and AxPcoords (Ax stands for Accelerated) which are highly optimized versions of Parafit and DistPCoA respectively.     

Recapitulating the evolution of Afrotheria: 57 genes and rare genomic changes (RGCs) consolidate their history

The decipherment of higher level relationships among the orders of Afrotheria – an extraordinary assumption in mammalian evolution – constitutes one of the major disputes in the evolutionary history of mammals. Recent comprehensive studies of various genomic data, including mitochondrial and nuclear DNA sequences, chromosomal syntenic associations and retroposon insertions support strongly the monophyly of Afrotheria. However, the relationships within Afrotheria have remained ambiguous and there is a necessity for a more sophisticated analysis (i.e. combination of gene phylogeny and Rare Genomic Changes (RGCs)), which could aid in the comprehension of the evolutionary history of this old group of mammals. The present study investigated the phylogenetic relationships within Afrotheria by analysing a data set of coding and non-coding sequences (～32 000 bp) comprising 57 orthologous genes and 31 RGCs, such as chromosomal associations and retroposon insertions, and re-evaluated a molecular timescale for afrotherian mammals using a Bayesian relaxed clock approach. The interordinal afrotherians phylogeny presented here contributed to the elucidation of the evolutionary history of this ancient clade of mammals, which is one of the most unorthodox proposals in mammalian biology. This is critical not only for understanding how Afrotheria evolved in Africa, but also to comprehend the early biogeographical history of placental mammals.     

SCRAPP: A tool to assess the diversity of microbial samples from phylogenetic placements

Microbial ecology research is currently driven by the continuously decreasing cost of DNA sequencing and the improving accuracy of data analysis methods. One such analysis method is phylogenetic placement, which establishes the phylogenetic identity of the anonymous environmental sequences in a sample by means of a given phylogenetic reference tree. However, assessing the diversity of a sample remains challenging, as traditional methods do not scale well with the increasing data volumes and/or do not leverage the phylogenetic placement information. Here, we present scrapp , a highly parallel and scalable tool that uses a molecular species delimitation algorithm to quantify the diversity distribution over the reference phylogeny for a given phylogenetic placement of the sample. scrapp employs a novel approach to cluster phylogenetic placements, called placement space clustering, to efficiently perform dimensionality reduction, so as to scale on large data volumes. Furthermore, it uses the phylogeny‐aware molecular species delimitation method mPTP to quantify diversity. We evaluated scrapp using both, simulated and empirical data sets. We use simulated data to verify our approach. Tests on an empirical data set show that scrapp ‐derived metrics can classify samples by their diversity‐correlated features equally well or better than existing, commonly used approaches. scrapp is available at https://github.com/pbdas/scrapp .