MotivationPhylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets.ResultsWe present RAxML-NG, a from-scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability compared with RAxML/ExaML. On taxon-rich datasets, RAxML-NG typically finds higher-scoring trees than IQTree, an increasingly popular recent tool for ML-based phylogenetic inference (although IQ-Tree shows better stability). Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and the recently introduced transfer bootstrap support metric.Availability and implementationThe code is available under GNU GPL at . RAxML-NG web service (maintained by Vital-IT) is available at .Supplementary information Supplementary data are available at Bioinformatics online.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

ModelTest-NG is a reimplementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest , last accessed September 2, 2019.

Hymenoptera (sawflies, wasps, ants, and bees) are one of four mega-diverse insect orders, comprising more than 153,000 described and possibly up to one million undescribed extant species [1, 2]. As parasitoids, predators, and pollinators, Hymenoptera play a fundamental role in virtually all terrestrial ecosystems and are of substantial economic importance [1, 3]. To understand the diversification and key evolutionary transitions of Hymenoptera, most notably from phytophagy to parasitoidism and predation (and vice versa) and from solitary to eusocial life, we inferred the phylogeny and divergence times of all major lineages of Hymenoptera by analyzing 3,256 protein-coding genes in 173 insect species. Our analyses suggest that extant Hymenoptera started to diversify around 281 million years ago (mya). The primarily ectophytophagous sawflies are found to be monophyletic. The species-rich lineages of parasitoid wasps constitute a monophyletic group as well. The little-known, species-poor Trigonaloidea are identified as the sister group of the stinging wasps (Aculeata). Finally, we located the evolutionary root of bees within the apoid wasp family "Crabronidae." Our results reveal that the extant sawfly diversity is largely the result of a previously unrecognized major radiation of phytophagous Hymenoptera that did not lead to wood-dwelling and parasitoidism. They also confirm that all primarily parasitoid wasps are descendants of a single endophytic parasitoid ancestor that lived around 247 mya. Our findings provide the basis for a natural classification of Hymenoptera and allow for future comparative analyses of Hymenoptera, including their genomes, morphology, venoms, and parasitoid and eusocial life styles.

Motivation: Phylogenies are important for fundamental biological research, but also have numerous applications in biotechnology, agriculture, and medicine. Finding the optimal tree under the popular maximum likelihood (ML) criterion is known to be NP-hard. Thus, highly optimized and scalable codes are needed to analyze constantly growing empirical datasets. Results: We present RAxML-NG, a from scratch re-implementation of the established greedy tree search algorithm of RAxML/ExaML. RAxML-NG offers improved accuracy, flexibility, speed, scalability, and usability. It compares favorably to IQ-Tree, an increasingly popular recent tool for ML-based phylogenetic inference. Finally, RAxML-NG introduces several new features, such as the detection of terraces in tree space and a the recently introduced transfer bootstrap support metric.