Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of ‘guilt by association’ was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info, a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.In December 2019, a series of cases of pneumonia of unknown origin appeared in Wuhan, China and on 7 January 2020, the virus responsible for the diseases was identified as a novel coronavirus, SARS-CoV-2 (ref. 1 ). The first SARS-CoV-2 genome was made publicly available on 10 January 2020 (refs. 2,3 ). Since then, the global scientific community, through an unprecedented effort, has sequenced and shared over 11 million genomes through GISAID (https://gisaid.org/), as of May 2022 (ref. 4 ). To keep track of the evolving genetic diversity of SARS-CoV-2, Rambaut

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To combat the ongoing COVID-19 pandemic, scientists have been conducting research at breakneck speeds, producing over 52,000 peer reviewed articles within the first 12 months. In contrast, a little over 1,000 peer reviewed articles were published within the first 12 months of the SARS-CoV-1 pandemic starting in 2002. In addition to publications, there has also been an upsurge in clinical trials to develop vaccines and treatments, scientific protocols to study SARS-CoV-2, methodology for epidemiological modeling, and datasets spanning molecular studies to social science research. One of the largest challenges has been keeping track of the vast amounts of newly generated disparate data and research that exist in independent repositories. To address this issue, we developed outbreak.info, which provides a standardized, searchable interface of heterogeneous data resources on COVID-19 and SARS-CoV-2. Unifying metadata from 14 data repositories, we have assembled a collection of over 200,000 publications, clinical trials, datasets, protocols, and other resources as of October 2021. We used a rigorous schema to enforce a consistent format across different data sources and resource types, and linked related resources where possible. This enables users to quickly retrieve information across data repositories, regardless of resource type or repository location. Outbreak.info also combines the combined research library with spatiotemporal genomics data on SARS-CoV-2 variants and epidemiological data on COVID-19 cases and deaths. The web interface provides interactive visualizations and reports to explore the unified data and generate hypotheses. In addition to providing a web interface, we also publish the data we have assembled and standardized in a high performance public API and an R package. Finally, we discuss the challenges inherent in combining metadata from scattered and heterogeneous resources and provide recommendations to streamline this process to aid scientific research.