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Large-scale reference genome sequencing projects for all of biodiversity are underway and common standards have been in place for some years to enable the understanding and sharing of sequence data. However, the metadata that describes the collection, processing and management of samples, and link to the associated sequencing and genome data, are not yet adequately developed and standardised for these projects. At the time of writing, the Darwin Tree of Life (DToL) Project is over two years into its ten-year ambition to sequence all described eukaryotic species in Britain and Ireland. We have sought consensus from a wide range of scientists across taxonomic domains to determine the minimal set of metadata that we collectively deem as critically important to accompany each sequenced specimen. These metadata are made available throughout the subsequent laboratory processes, and once collected, need to be adequately managed to fulfil the requirements of good data management practice. Due to the size and scale of management required, software tools are needed. These tools need to implement rigorous development pathways and change management procedures to ensure that effective research data management of key project and sample metadata is maintained. Tracking of sample properties through the sequencing process is handled by Lab Information Management Systems (LIMS), so publication of the sequenced data is achieved via technical integration of LIMS and data management tools. Discussions with community members on how metadata standards need to be managed within large-scale programmes is a priority in the planning process. Here we report on the standards we developed with respect to a robust and reusable mechanism of metadata collection, in the hopes that other projects forthcoming or underway will adopt these practices for metadata.

Depression, cardiovascular diseases and diabetes are among the major non-communicable diseases, leading to significant disability and mortality worldwide. These diseases may share environmental and genetic determinants associated with multimorbid patterns. Stressful early-life events are among the primary factors associated with the development of mental and physical diseases. However, possible causative mechanisms linking early life stress (ELS) with psycho-cardio-metabolic (PCM) multi-morbidity are not well understood. This prevents a full understanding of causal pathways towards the shared risk of these diseases and the development of coordinated preventive and therapeutic interventions., This work is supported by the European Union’s Horizon 2020 research and innovation programme (grant n ̊ 848158).

This deliverable describes the early life stress (ELS) and psycho-cardiometabolic multi-morbidity (PCM) related metadata standards and formats that have been or will be generated as part of the EarlyCause project. This is the second version of the data management plan which follows on from version 1 which was submitted in Month 6, as deliverable D2.1. The development of metadata standards and the use of appropriate ontologies will contribute to how data are displayed and available for search in the EarlyCause research platform and web portal. The recommended databases to be used in the project for the deposition of different types of data leading to long-term sustainability will also be discussed. This will enable data generated and submitted to be compliant with FAIR (findable, accessible, interoperable and reusable) principles1. The project aims to make relevant data available for reusability by the community through public and specialised data repositories., This deliverable has been produced in the context of the EarlyCause ("Causative mechanisms & integrative models linking early-life-stress to psycho-cardio-metabolic multi-morbidity") Research and Innovation Action, funded by the European Union's Horizon 2020 programme (grant agreement No 848158).

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena), provided by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), has for almost forty years continued in its mission to freely archive and present the world's public sequencing data for the benefit of the entire scientific community and for the acceleration of the global research effort. Here we highlight the major developments to ENA services and content in 2020, focussing in particular on the recently released updated ENA browser, modernisation of our release process and our data coordination collaborations with specific research communities.

We present the Aquatic Symbiosis Genomics Project, a global collaboration to generate high quality genome sequences for a wide range of eukaryotes and their microbial symbionts. Launched under the Symbiosis in Aquatic Systems Initiative of the Gordon and Betty Moore Foundation, the ASG Project brings together researchers from across the globe who hope to use these reference genomes to augment and extend their analyses of the dynamics, mechanisms and environmental importance of symbiosis. Applying large-scale, high-throughput sequencing and assembly technologies, the ASG collaboration will assemble and annotate the genomes of 500 symbiotic organisms – both the “hosts” and the microbial symbionts with which they associate. These data will be released openly to benefit all who work on symbiosis, from conservation geneticists to those interested in the origin of the eukaryotic cell.

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena), provided from EMBL-EBI, has for more than three decades been responsible for archiving the world's public sequencing data and presenting this important resource to the scientific community to support and accelerate the global research effort. Here, we outline ENA services and content in 2018 and provide an overview of a selection of focus areas of development work: extending data coordination services around ENA, sequence submissions through template expansion, early pre-submission validation tools and our move towards a new browser and retrieval infrastructure.

The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) is a repository for the submission, maintenance and presentation of nucleotide sequence data and related sample and experimental information. In this article we report on ENA in 2015 regarding general activity, notable published data sets and major achievements. This is followed by a focus on sustainable biocuration of functional annotation, an area which has particularly felt the pressure of sequencing growth. The importance of functional annotation, how it can be submitted and the shifting role of the biocurator in the context of increasing volumes of data are all discussed.

The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) offers a rich platform for data sharing, publishing and archiving and a globally comprehensive data set for onward use by the scientific community. With a broad scope spanning raw sequencing reads, genome assemblies and functional annotation, the resource provides extensive data submission, search and download facilities across web and programmatic interfaces. Here, we outline ENA content and major access modalities, highlight major developments in 2016 and outline a number of examples of data reuse from ENA.

For 35 years the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena) has been responsible for making the world’s public sequencing data available to the scientific community. Advances in sequencing technology have driven exponential growth in the volume of data to be processed and stored and a substantial broadening of the user community. Here, we outline ENA services and content in 2017 and provide insight into a selection of current key areas of development in ENA driven by challenges arising from the above growth.

The Hedgehog (Hh) signaling pathway promotes pattern formation and cell proliferation in Drosophila and vertebrates. Hh is a ligand that binds and represses the Patched (Ptc) receptor and thereby releases the latent activity of the multipass membrane protein Smoothened (Smo), which is essential for transducing the Hh signal. In Caenorhabditis elegans, the Hh signaling pathway has undergone considerable divergence. Surprisingly, obvious Smo and Hh homologs are absent whereas PTC, PTC-related (PTR), and a large family of nematode Hh-related (Hh-r) proteins are present. We find that the number of PTC-related and Hh-r proteins has expanded in C. elegans, and that this expansion occurred early in Nematoda. Moreover, the function of these proteins appears to be conserved in Caenorhabditis briggsae. Given our present understanding of the Hh signaling pathway, the absence of Hh and Smo raises many questions about the evolution and the function of the PTC, PTR, and Hh-r proteins in C. elegans. To gain insights into their roles, we performed a global survey of the phenotypes produced by RNA-mediated interference (RNAi). Our study reveals that these genes do not require Smo for activity and that they function in multiple aspects of C. elegans development, including molting, cytokinesis, growth, and pattern formation. Moreover, a subset of the PTC, PTR, and Hh-r proteins have the same RNAi phenotypes, indicating that they have the potential to participate in the same processes.

The nematode Caenorhabditis elegans is widely used as a model organism for studying many fundamental aspects of development and cell biology, including processes underlying human disease. The genome of C. elegans encodes over 19,000 protein-coding genes and hundreds of non-coding RNAs. The availability of whole genome sequence has facilitated the development of high throughput techniques for elucidating the function of individual genes and gene products. Furthermore, attempts can now be made to integrate these substantial functional genomics data collections and to understand at a global level how the flow of genomic information that is at the core of the central dogma leads to the development of a multicellular organism.

The Consensus Coding Sequence (CCDS) project (http://www.ncbi.nlm.nih.gov/CCDS/) is a collaborative effort to maintain a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assemblies by the National Center for Biotechnology Information (NCBI) and Ensembl genome annotation pipelines. Identical annotations that pass quality assurance tests are tracked with a stable identifier (CCDS ID). Members of the collaboration, who are from NCBI, the Wellcome Trust Sanger Institute and the University of California Santa Cruz, provide coordinated and continuous review of the dataset to ensure high-quality CCDS representations. We describe here the current status and recent growth in the CCDS dataset, as well as recent changes to the CCDS web and FTP sites. These changes include more explicit reporting about the NCBI and Ensembl annotation releases being compared, new search and display options, the addition of biologically descriptive information and our approach to representing genes for which support evidence is incomplete. We also present a summary of recent and future curation targets.

Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions., National Human Genome Research Institute (U.S.) (Grant number 1U54HG004555-01), Wellcome Trust (London, England) (Grant number WT062023), Wellcome Trust (London, England) (Grant number WT077198)

The European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena), maintained at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) provides freely accessible services, both for deposition of, and access to, open nucleotide sequencing data. Open scientific data are of paramount importance to the scientific community and contribute daily to the acceleration of scientific advance. Here, we outline the major updates to ENA’s services and infrastructure that have been delivered over the past year.

The European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena) at the European Molecular Biology Laboratory’s European Bioinformatics Institute provides open and freely available data deposition and access services across the spectrum of nucleotide sequence data types. Making the world’s public sequencing datasets available to the scientific community, the ENA represents a globally comprehensive nucleotide sequence resource. Here, we outline ENA services and content in 2019 and provide an insight into selected key areas of development in this period.

The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers., National Institutes of Health (U.S.) (Grant 5U54HG004555), Wellcome Trust (London, England) (Grant WT098051)

The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learned. As a component of the Platform, the SARS-CoV-2 Data Hubs enabled the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.