Thus far, optical recording of neuronal activity in freely behaving animals has been limited to a thin axial range. We present a head-mounted miniaturized light-field microscope (MiniLFM) capable of capturing neuronal network activity within a volume of 700 × 600 × 360 µm at 16 Hz in the hippocampus of freely moving mice. We demonstrate that neurons separated by as little as ~15 µm and at depths up to 360 µm can be discriminated.

Light-field microscopy (LFM) is a scalable approach for volumetric Ca imaging with high volumetric acquisition rates (up to 100 Hz). Although the technology has enabled whole-brain Ca imaging in semi-transparent specimens, tissue scattering has limited its application in the rodent brain. We introduce seeded iterative demixing (SID), a computational source-extraction technique that extends LFM to the mammalian cortex. SID can capture neuronal dynamics in vivo within a volume of 900 × 900 × 260 μm located as deep as 380 μm in the mouse cortex or hippocampus at a 30-Hz volume rate while discriminating signals from neurons as close as 20 μm apart, at a computational cost three orders of magnitude less than that of frame-by-frame image reconstruction. We expect that the simplicity and scalability of LFM, coupled with the performance of SID, will open up a range of applications including closed-loop experiments.

The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab—or protocol—specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how samples can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared (https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 and https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.

Cnidaria (sea anemones, jellyfish, corals and hydra) form a close sister group to Bilateria. Within this clade, the sea anemone Nematostella vectensis has emerged as a slow evolving model for investigating characteristics of the cnidarian-bilaterian common ancestor, which diverged near the Cambrian explosion. Here, using long read sequencing and high throughput chromosome conformation capture, we generate high quality chromosome-level genome assemblies for N. vectensis and the closely related edwardsiid sea anemone, Scolanthus callimorphus. In both cases we find a robust set of 15 chromosomes comprising a stable linkage group detectable within all major clades of sequenced cnidarian genomes. Further, both genomes show remarkable chromosomal conservation with chordates. In contrast with Bilateria, we report that extended Hox and NK gene clusters are chromosomally linked but do not retain a tight spatial conservation. Accordingly, there is a lack of evidence for topologically associated domains, which have been implicated in the evolutionary pressure to retain tight microsyntenic gene clusters. We also uncover ultra-conserved noncoding elements at levels previously undetected in non-chordate lineages. Both genomes are accessible through an actively updated genome browser and database at https://simrbase.stowers.org

Transposable elements (TEs) are stretches of DNA that copy themselves within host genomes. They have been found in almost all eukaryotes and in most bacteria investigated so far (Biémont & Vieira, 2006; Wicker et al., 2007). TEs are important mutagens, which generate novel phenotypic variation; e.g., in Drosophila melanogaster an estimated 50%-80% of the observed mutations are

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.

Nanopore sequencers can select which DNA molecules to sequence, rejecting a molecule after analysis of a small initial part. Currently, selection is based on predetermined regions of interest that remain constant throughout an experiment. Sequencing efforts, thus, cannot be re-focused on molecules likely contributing most to experimental success. Here we present BOSS-RUNS, an algorithmic framework and software to generate dynamically updated decision strategies. We quantify uncertainty at each genome position with real-time updates from data already observed. For each DNA fragment, we decide whether the expected decrease in uncertainty that it would provide warrants fully sequencing it, thus optimizing information gain. BOSS-RUNS mitigates coverage bias between and within members of a microbial community, leading to improved variant calling; for example, low-coverage sites of a species at 1% abundance were reduced by 87.5%, with 12.5% more single-nucleotide polymorphisms detected. Such data-driven updates to molecule selection are applicable to many sequencing scenarios, such as enriching for regions with increased divergence or low coverage, reducing time-to-answer.

Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, as well as being part of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. >100,000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software is available from https://github.com/NicolaDM/phastSim and allows easy integration with other Python packages as well as a variety of evolutionary models, including new ones that we developed to more realistically model SARS-CoV-2 genome evolution.