Multiple origins of sexual dichromatism and aposematism within large carpenter bees : SEXUAL DICHROMATISM AND APOSEMATISM INXYLOCOPA

Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Although massively parallel sequencing has facilitated large-scale DNA sequencing, comparisons among distantly related species rely upon small portions of the genome that are easily aligned. Methods are needed to efficiently obtain comparable DNA fragments prior to massively parallel sequencing, particularly for biologists working with non-model organisms. We introduce a new class of molecular marker, anchored by ultraconserved genomic elements (UCEs), that universally enable target enrichment and sequencing of thousands of orthologous loci across species separated by hundreds of millions of years of evolution. Our analyses here focus on use of UCE markers in Amniota because UCEs and phylogenetic relationships are well-known in some amniotes. We perform an in silico experiment to demonstrate that sequence flanking 2030 UCEs contains information sufficient to enable unambiguous recovery of the established primate phylogeny. We extend this experiment by performing an in vitro enrichment of 2386 UCE-anchored loci from nine, non-model avian species. We then use alignments of 854 of these loci to unambiguously recover the established evolutionary relationships within and among three ancient bird lineages. Because many organismal lineages have UCEs, this type of genetic marker and the analytical framework we outline can be applied across the tree of life, potentially reshaping our understanding of phylogeny at many taxonomic levels.

Motivation: The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed ‘bipartitions’. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent. Results: We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL’s running time is O ( n 2 k | X | 2 ) , and ASTRAL-II’s running time is O ( n k | X | 2 ) , where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space. Availability and implementation: ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/. Contact: smirarab@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.