Reversing the field: Do terrestrial geomagnetic field reversals have an effect on Earth's climate? Cooper et al. created a precisely dated radiocarbon record around the time of the Laschamps geomagnetic reversal about 41,000 years ago from the rings of New Zealand swamp kauri trees. This record reveals a substantial increase in the carbon-14 content of the atmosphere culminating during the period of weakening magnetic field strength preceding the polarity switch. The authors modeled the consequences of this event and concluded that the geomagnetic field minimum caused substantial changes in atmospheric ozone concentration that drove synchronous global climate and environmental shifts. Science , this issue p. 811

The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA ‘reads’) against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30–80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software—BWA-aln, BWA-mem, NovoAlign and Bowtie2—and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.

We present Genozip, a universal and fully featured compression software for genomic data. Genozip is designed to be a general-purpose software and a development framework for genomic compression by providing five core capabilities—universality (support for all common genomic file formats), high compression ratios, speed, feature-richness and extensibility. Genozip delivers high-performance compression for widelyused genomic data formats in genomics research, namely FASTQ, SAM/BAM/CRAM, VCF, GVF, FASTA, PHYLIP and 23andMe formats. Our test results show that Genozip is fast and achieves greatly improved compression ratios, even when the files are already compressed. Further, Genozip is architected with a separation of the Genozip Framework from file-format-specific Segmenters and data-type-specific Codecs. With this, we intend for Genozip to be a general-purpose compression platform where researchers can implement compression for additional file formats, as well as new codecs for data types or fields within files, in the future. We anticipate that this will ultimately increase the visibility and adoption of these algorithms by the user community, thereby accelerating further innovation in this space. Genozip is written in C. The code is open-source and available on http://www.genozip.com. The package is free for non-commercial use. It is distributed through the Conda package manager, github, and as a Docker container on DockerHub. Genozip is tested on Linux, Mac and Windows. Supplementary data are available at Bioinformatics online.

Adopting the name Canis dingo for the Dingo to explicitly denote a species-level taxon separate from other canids was suggested by Crowther et al. (2014) as a means to eliminate taxonomic instability and contention. However, Jackson et al. (2017), using standard taxonomic and nomenclatural approaches and principles, called instead for continued use of the nomen C. familiaris for all domestic dogs and their derivatives, including the Dingo. (This name, C. familiaris, is applied to all dogs that derive from the domesticated version of the Gray Wolf, Canis lupus, based on nomenclatural convention.) The primary reasons for this call by Jackson et al. (2017) were: (1) a lack of evidence to show that recognizing multiple species amongst the dog, including the Dingo and New Guinea Singing Dog, was necessary taxonomically, and (2) the principle of nomenclatural priority (the name familiaris Linnaeus, 1758, antedates dingo Meyer, 1793). Overwhelming current evidence from archaeology and genomics indicates that the Dingo is of recent origin in Australia and shares immediate ancestry with other domestic dogs as evidenced by patterns of genetic and morphological variation. Accordingly, for Smith et al. (2019) to recognise Canis dingo as a distinct species, the onus was on them to overturn current interpretations of available archaeological, genomic, and morphological datasets and instead show that Dingoes have a deeply divergent evolutionary history that distinguishes them from other named forms of Canis (including C. lupus and its domesticated version, C. familiaris). A recent paper by Koepfli et al. (2015) demonstrates exactly how this can be done in a compelling way within the genus Canis—by demonstrating deep evolutionary divergence between taxa, on the order of hundreds of thousands of years, using data from multiple genetic systems. Smith et al. (2019) have not done this; instead they have misrepresented the content and conclusions of Jackson et al. (2017), and contributed extraneous arguments that are not relevant to taxonomic decisions. Here we dissect Smith et al. (2019), identifying misrepresentations, to show that ecological, behavioural and morphological evidence is insufficient to recognise Dingoes as a separate species from other domestic dogs. We reiterate: the correct binomial name for the taxon derived from Gray Wolves (C. lupus) by passive and active domestication, including Dingoes and other domestic dogs, is Canis familiaris. We are strongly sympathetic to arguments about the historical, ecological, cultural, or other significance of the Dingo, but these are issues that will have to be considered outside of the more narrow scope of taxonomy and nomenclature.

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied on genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has probably been underappreciated in the evolutionary history of humans and suggest that our current understanding of the tempo and mode of selection in natural populations may be inaccurate.