Species Tree Estimation from Genome-Wide Data with guenomu

Rooting Species Trees Using Gene Tree-Species Tree Reconciliation

Interpreting phylogenetic trees requires a root, which provides the direction of evolution and polarizes ancestor-descendant relationships. But inferring the root using genetic data is difficult, particularly in cases where the closest available outgroup is only distantly related, which are common for microbes. In this chapter, we present a workflow for estimating rooted species trees and the evolutionary history of the gene families that evolve within them using probabilistic gene tree-species tree reconciliation. We illustrate the pipeline using a small dataset of prokaryotic genomes, for which the example scripts can be run using modest computer resources. We describe the rooting method used in this work in the context or other rooting strategies and discuss some of the limitations and opportunities presented by probabilistic gene tree-species tree reconciliation methods.

Relative Time Inference Using Lateral Gene Transfers

Many organisms are able to incorporate exogenous DNA into their genomes. This process, called lateral gene transfer (LGT), has the potential to benefit the recipient organism by providing useful coding sequences, such as antibiotic resistance genes or enzymes which expand the organism's metabolic niche. For evolutionary biologists, LGTs have often been considered a nuisance because they complicate the reconstruction of the underlying species tree that many analyses aim to recover. However, LGT events between distinct organisms harbor information on the relative divergence time of the donor and recipient lineages. As a result transfers provide a novel and as yet mostly unexplored source of information to determine the order of divergence of clades, with the potential for absolute dating if linked to the fossil record.

TREEasy: An automated workflow to infer gene trees, species trees, and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user-friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python-TREEasy-that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ-Tree), species inference from concatenated data (with IQ-Tree and RaxML-NG), species tree inference from gene trees (with ASTRAL, MP-EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the "WGD clade" of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology (https://github.com/MaoYafei/TREEasy).

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

An empirical assessment of a single family-wide hybrid capture locus set at multiple evolutionary timescales in Asteraceae

PREMISE

Hybrid capture with high-throughput sequencing (Hyb-Seq) is a powerful tool for evolutionary studies. The applicability of an Asteraceae family-specific Hyb-Seq probe set and the outcomes of different phylogenetic analyses are investigated here.

METHODS

Hyb-Seq data from 112 Asteraceae samples were organized into groups at different taxonomic levels (tribe, genus, and species). For each group, data sets of non-paralogous loci were built and proportions of parsimony informative characters estimated. The impacts of analyzing alternative data sets, removing long branches, and type of analysis on tree resolution and inferred topologies were investigated in tribe Cichorieae.

RESULTS

Alignments of the Asteraceae family-wide Hyb-Seq locus set were parsimony informative at all taxonomic levels. Levels of resolution and topologies inferred at shallower nodes differed depending on the locus data set and the type of analysis, and were affected by the presence of long branches.

DISCUSSION

The approach used to build a Hyb-Seq locus data set influenced resolution and topologies inferred in phylogenetic analyses. Removal of long branches improved the reliability of topological inferences in maximum likelihood analyses. The Astereaceae Hyb-Seq probe set is applicable at multiple taxonomic depths, which demonstrates that probe sets do not necessarily need to be lineage-specific.

Microbial sequence typing in the genomic era

Next-generation sequencing (NGS), also known as high-throughput sequencing, is changing the field of microbial genomics research. NGS allows for a more comprehensive analysis of the diversity, structure and composition of microbial genes and genomes compared to the traditional automated Sanger capillary sequencing at a lower cost. NGS strategies have expanded the versatility of standard and widely used typing approaches based on nucleotide variation in several hundred DNA sequences and a few gene fragments (MLST, MLVA, rMLST and cgMLST). NGS can now accommodate variation in thousands or millions of sequences from selected amplicons to full genomes (WGS, NGMLST and HiMLST). To extract signals from high-dimensional NGS data and make valid statistical inferences, novel analytic and statistical techniques are needed. In this review, we describe standard and new approaches for microbial sequence typing at gene and genome levels and guidelines for subsequent analysis, including methods and computational frameworks. We also present several applications of these approaches to some disciplines, namely genotyping, phylogenetics and molecular epidemiology.