European Molecular Biology Laboratory's European Bioinformatics Institute Research Articles

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2023.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the latest developments in the services provided by EMBL-EBI data resources to scientific communities globally. These developments aim to ensure EMBL-EBI resources meet the current and future needs of these scientific communities, accelerating the impact of open biological data for all.

The European Nucleotide Archive in 2023.

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena) is maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). The ENA is one of the three members of the International Nucleotide Sequence Database Collaboration (INSDC). It serves the bioinformatics community worldwide via the submission, processing, archiving and dissemination of sequence data. The ENA supports data types ranging from raw reads, through alignments and assemblies to functional annotation. The data is enriched with contextual information relating to samples and experimental configurations. In this article, we describe recent progress and improvements to ENA services. In particular, we focus upon three areas of work in 2023: FAIRness of ENA data, pandemic preparedness and foundational technology. For FAIRness, we have introduced minimal requirements for spatiotemporal annotation, created a metadata-based classification system, incorporated third party metadata curations with archived records, and developed a new rapid visualisation platform, the ENA Notebooks. For foundational enhancements, we have improved the INSDC data exchange and synchronisation pipelines, and invested in site reliability engineering for ENA infrastructure. In order to support genomic surveillance efforts, we have continued to provide ENA services in support of SARS-CoV-2 data mobilisation and have adapted these for broader pathogen surveillance efforts.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing thefoundations for tools and research insights that transform fields across the life sciences.

The European Nucleotide Archive in 2022.

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena), maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), offers those producing data an open and supported platform for the management, archiving, publication, and dissemination of data; and to the scientific community as a whole, it offers a globally comprehensive data set through a host of data discovery and retrieval tools. Here, we describe recent updates to the ENA's submission and retrieval services as well as focused efforts to improve connectivity, reusability, and interoperability of ENA data and metadata.

The European Nucleotide Archive in 2021.

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.

From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering.

The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets.

The FAANG Data Portal: Global, Open-Access, "FAIR", and Richly Validated Genotype to Phenotype Data for High-Quality Functional Annotation of Animal Genomes.

The Functional Annotation of ANimal Genomes (FAANG) project is a worldwide coordinated action creating high-quality functional annotation of farmed and companion animal genomes. The generation of a rich genome-to-phenome resource and supporting informatic infrastructure advances the scope of comparative genomics and furthers the understanding of functional elements. The project also provides terrestrial and aquatic animal agriculture community powerful resources for supporting improvements to farmed animal production, disease resistance, and genetic diversity. The FAANG Data Portal (https://data.faang.org) ensures Findable, Accessible, Interoperable and Reusable (FAIR) open access to the wealth of sample, sequencing, and analysis data produced by an ever-growing number of FAANG consortia. It is developed and maintained by the FAANG Data Coordination Centre (DCC) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI). FAANG projects produce a standardised set of multi-omic assays with resulting data placed into a range of specialised open data archives. To ensure this data is easily findable and accessible by the community, the portal automatically identifies and collates all submitted FAANG data into a single easily searchable resource. The Data Portal supports direct download from the multiple underlying archives to enable seamless access to all FAANG data from within the portal itself. The portal provides a range of predefined filters, powerful predictive search, and a catalogue of sampling and analysis protocols and automatically identifies publications associated with any dataset. To ensure all FAANG data submissions are high-quality, the portal includes powerful contextual metadata validation and data submissions brokering to the underlying EMBL-EBI archives. The portal will incorporate extensive new technical infrastructure to effectively deliver and standardise FAANG's shift to single-cellomics, cell atlases, pangenomes, and novel phenotypic prediction models. The Data Portal plays a key role for FAANG by supporting high-quality functional annotation of animal genomes, through open FAIR sharing of data, complete with standardised rich metadata. Future Data Portal features developed by the DCC will support new technological developments for continued improvement for FAANG projects.

The European Nucleotide Archive in 2020.

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.

The international nucleotide sequence database collaboration.

The International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/) has been the core infrastructure for collecting and providing nucleotide sequence data and metadata for >30 years. Three partner organizations, the DNA Data Bank of Japan (DDBJ) at the National Institute of Genetics in Mishima, Japan; the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK; and GenBank at National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health in Bethesda, Maryland, USA have been collaboratively maintaining the INSDC for the benefit of not only science but all types of community worldwide.

ELIXIR: Data for Life – Coordinating life science data and services across Europe

ELIXIR unites Europe’s leading life science organisations in managing and safeguarding the increasing volume of data being generated by publicly funded research. It coordinates, integrates and sustains bioinformatics resources across its 22 member states, plus EMBL-EBI (European Molecular Biology Laboratory - European Bioinformatics Institute), and enables end users to access services and data that are vital for their research. ELIXIR's remit spans the full breadth of life science data, including data related to human health, food production (agriculture, farming, aquaculture) and the environment (e.g. pollution remediation, ecology), all of clear socio-economic benefit. As a result, ELIXIR contributes to the delivery of several sustainable development goals. This poster will introduce ELIXIR and describe the contribution it can make to coordinating data and services relevant to biodiversity. The poster will set the context for how molecularly-derived biodiversity occurrence data can significantly enhance resources such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biogeographic Information System (OBIS), e.g. by filling in acute gaps in our knowledge of species across realms.

From trainees to trainers to instructors: Sustainably building a national capacity in bioinformatics training.

Demand for training life scientists in bioinformatics skills led to the development of a train-the-trainer collaboration between the European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI) and 2 Australian organisations, Bioplatforms Australia and Commonwealth Scientific and Industrial Research Organisation (CSIRO) in 2012. The goal of the collaboration was to establish a group of trained instructors who could develop and deliver short bioinformatics courses nationally. A train-the-trainer course introduces instructors to aspects of andragogy and evidence-based learning principles to help them better design, develop, and deliver high-quality training. Since then, both the number of trainers in the network and the course portfolio have grown. Best practises have been developed and shared between the Australian cohort and EMBL-EBI to address common challenges in bioinformatics training. The Australian trainer cohort undertook a train-the-trainer instructor course, again with EMBL-EBI, and subsequently successfully delivered train-the-trainer courses to interested bioinformatics trainers within Australia. We conclude that a train-the-trainer approach can help build national capacity and maintain a critical mass of trained instructors.

Eleven quick tips to build a usable REST API for life sciences.

In recent years, technological advances have greatly expanded the range of data types generated by life sciences researchers. These span domains such as molecular structures, nucleotide and protein sequences, metabolomics, and chemogenomics, resulting in hundreds of public resources holding diverse data sets for reuse in multiple formats [1]. Most resources focus on a specific data type, yet their value for researchers is enhanced once cross-referenced and combined with expert annotation and knowledge. Cross-referencing has increasingly been achieved by implementing website application programming interfaces (web APIs), providing programming-language−agnostic methods to access online resources. Web APIs enable dynamic data exchange between resources, augment websites with additional data, and can provide access to large data sets. Web APIs also enhance adherence to FAIR data principles by making data Findable, Accessible, Interoperable, and Reusable [2], thus increasing the value of those resources. Representational state transfer (REST) [3] is a popular method for providing interoperability between a client and server [4] using the hypertext transfer protocol (HTTP), the same building block as the world wide web, [5] and a common exchange format, e.g., JavaScript Object Notation (JSON) [6]. REST APIs are considered easier to develop than previous web-service standards, e.g., Simple Object Access Protocol (SOAP). However, REST specifies a set of requirements that any implementation of a REST API must address. Although well-known resources such as the World Wide Web Consortium (W3C) (https://www.w3.org) and the Internet Engineering Task Force (IETF) (https://www.ietf.org/) provide guidance on how to implement such a service, they can be difficult to understand and may have limited documentation. We present here 11 quick tips for creating and maintaining REST web APIs that were developed while implementing various web APIs (https://www.ebi.ac.uk/services) for European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI)’s data resources.

Uniform resolution of compact identifiers for biomedical data

Most biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however be prefixed with a namespace identifier, providing global uniqueness. Such “compact identifiers” have been widely used in biomedical informatics to support global resource identification with local identifier assignment. We report here on our project to provide robust support for machine-resolvable, persistent compact identifiers in biomedical data citation, by harmonizing the Identifiers.org and N2T.net (Name-To-Thing) meta-resolvers and extending their capabilities. Identifiers.org services hosted at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), and N2T.net services hosted at the California Digital Library (CDL), can now resolve any given identifier from over 600 source databases to its original source on the Web, using a common registry of prefix-based redirection rules. We believe these services will be of significant help to publishers and others implementing persistent, machine-resolvable citation of research data.

The international nucleotide sequence database collaboration.

For more than 30 years, the International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/) has been committed to capturing, preserving and providing access to comprehensive public domain nucleotide sequence and associated metadata which enables discovery in biomedicine, biodiversity and biological sciences. Since 1987, the DNA Data Bank of Japan (DDBJ) at the National Institute for Genetics in Mishima, Japan; the European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) in Hinxton, UK; and GenBank at National Center for Biotechnology Information (NCBI), National Library of Medicine, National Institutes of Health in Bethesda, Maryland, USA have worked collaboratively to enable access to nucleotide sequence data in standardized formats for the worldwide scientific community. In this article, we reiterate the principles of the INSDC collaboration and briefly summarize the trends of the archival content.

Differential expression of molecular rust resistance components have distinctive profiles in Coffea arabica - Hemileia vastatrix interactions

Countering the economic hurdle caused by coffee leaf rust disease is most appealing at this time as it has posed a major threat to coffee production around the world. Establishing differential expression profiles at different times following pathogen invasion in both innate and acquired immunities unlocks the molecular components of resistance and susceptibility. Suppression subtractive hybridization (SSH) was used to identify genes differentially over-expressed and repressed during incompatible and compatible interactions between Coffea arabica and Hemileia vastatrix. From 433 clones of expressed sequence tags (ESTs) sequenced, 352 were annotated and categorized of which the proportion of genes expressed during compatible interaction were relatively smaller. The result showed upregulation and downregulation of various genes at 12 and 24 h after pathogen inoculation in both interactions. The use of four different databases in searching for gene homology resulted in different number of similar sequences. BLASTx against EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) database being with the maximum (100%) hits for all the annotated sequences. RT-qPCR analysis of seven resistance-signaling genes showed similar expression patterns for most of the genes in both interactions, indicating these genes are involved in basal (non-specific) defense during which immune reactions are similar. Using SSH, we identified different types of resistance related genes that could be used for further studies towards resistant cultivar development. The potential role of some of the resistance related proteins found were also discussed.

Reminder to deposit DNA sequences

As members of the Advisory Committee to the International Nucleotide Sequence Database Collaboration (INSDC), which includes the DNA Data Bank of Japan (DDBJ), ENA, and GenBank databases, we wish to remind the research community of the importance of depositing complete DNA-sequence data in these databases upon publication of their results [see also S. L. Salzberg et al., Nature , (2016)]. Indeed, most journals demand a database accession number as a condition of publication. Access to the INSDC's databases is free and unrestricted ([ 1 ][1]), enabling researchers to plan experiments and to analyze existing data. As original contributions, deposited data form part of the scientific record and are citable in the literature. Authors can also correct and update their data. These amended records may be removed from the next database release but still remain permanently available by accession number. INSDC has also created major new repositories for large data collections, notably the Sequence Read Archive at the National Center for Biotechnology Information (NCBI) ([ 2 ][2]), the DDBJ Sequence Read Archive ([ 3 ][3]), and the European Nucleotide Archive at the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) ([ 4 ][4]). These archive raw data from sequencing experiments, a crucial facility for reproducibility and reuse. For papers dependent on sequence data from human subjects, unrestricted data release may not be possible. In these cases, we would encourage journal editors to insist on data sharing through other repositories that are not part of INSDC, such as NCBI's Database of Genotype and Phenotype ([ 5 ][5]), EMBL-EBI's European Genome-phenome Archive ([ 6 ][6]), or DDBJ's Japanese Genotype-phenotype Archive ([ 7 ][7]). ![Figure][8] IMAGE: BEHOLDINGEYE/[ISTOCKPHOTO.COM][9] 1. [↵][10]1. G. Cochrane, 2. I. Karsch-Mizrachi, 3. T. Takagi , International Nucleotide Sequence Database Collaboration, Nucleic Acids Res 44, D48 (2016). [OpenUrl][11] 2. [↵][12][www.ncbi.nlm.nih.gov/sra][13]. 3. [↵][14] . 4. [↵][15][www.ebi.ac.uk/ena][16]. 5. [↵][17][www.ncbi.nlm.nih.gov/gap][18]. 6. [↵][19][www.ebi.ac.uk/ega/home][20]. 7. [↵][21] . [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: pending:yes [9]: http://ISTOCKPHOTO.COM [10]: #xref-ref-1-1 View reference 1 in text [11]: {openurl}?query=rft.jtitle%253DInternational%2BNucleotide%2BSequence%2BDatabase%2BCollaboration%26rft.volume%253D44%26rft.spage%253DD48%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [12]: #xref-ref-2-1 View reference 2 in text [13]: http://www.ncbi.nlm.nih.gov/sra [14]: #xref-ref-3-1 View reference 3 in text [15]: #xref-ref-4-1 View reference 4 in text [16]: http://www.ebi.ac.uk/ena [17]: #xref-ref-5-1 View reference 5 in text [18]: http://www.ncbi.nlm.nih.gov/gap [19]: #xref-ref-6-1 View reference 6 in text [20]: http://www.ebi.ac.uk/ega/home [21]: #xref-ref-7-1 View reference 7 in text

Allergen cross-reactivity in allergic rhinitis and oral-allergy syndrome: a bioinformatic protein sequence analysis.

Clinical allergy cross-reactivity that is seen with related inhalant allergens or between unrelated inhalant allergens and foods in oral allergy syndrome (OAS) remains poorly understood. The goal of this study is to determine whether clinical cross-reactivity can be identified from primary protein sequences in allergy epitopes and food proteins. High-throughput analysis was performed by assembling all known allergy epitopes within the Immune Epitope Database (IEDB; http://www.iedb.org) for 5 common species from 5 inhalant allergen subclasses and comparing their protein sequences to each other, as well as to sequences of intact proteins from known cross-reactive foods in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) protein database (http://www.uniprot.org) that have been implicated in OAS. Computational methods were employed to allow for exact matching, gaps, and similar amino acids using multiple algorithms. A phylogenetic tree was created to determine evolutionary relationships between cross-reactive epitopes in OAS. Twenty-three common inhalant allergens had 4429 unique epitopes; the 19 foods implicated in OAS had 9497 protein sequences. The Basic Local Alignment Search Tool (BLAST) algorithm identified interclass and intraclass sequence similarities for the 5 inhalant allergy classes with high similarity for mites, grasses, and trees. Analysis of OAS proteins identified 104 matches to inhalant allergy epitopes that are known to cross-react. The phylogenetic tree displayed relationships that mostly followed organism phylogeny. Use of primary protein sequences was successful in explaining clinical allergy cross-reactivity. Clinical correlation is needed for use of these epitopes as diagnostic or therapeutic entities for patients with cross-reactive allergic disease.

A Single Rainbow Trout Cobalamin-binding Protein Stands in for Three Human Binders

Cobalamin uptake and transport in mammals are mediated by three cobalamin-binding proteins: haptocorrin, intrinsic factor, and transcobalamin. The nature of cobalamin-binding proteins in lower vertebrates remains to be elucidated. The aim of this study was to characterize the cobalamin-binding proteins of the rainbow trout (Oncorhynchus mykiss) and to compare their properties with those of the three human cobalamin-binding proteins. High cobalamin-binding capacity was found in trout stomach (210 pmol/g), roe (400 pmol/g), roe fluid (390 nmol/liter), and plasma (2500 nmol/liter). In all cases, it appeared to be the same protein based on analysis of partial sequences and immunological responses. The trout cobalamin-binding protein was purified from roe fluid, sequenced, and further characterized. Like haptocorrin, the trout cobalamin-binding protein was stable at low pH and had a high binding affinity for the cobalamin analog cobinamide. Like haptocorrin and transcobalamin, the trout cobalamin-binding protein was present in plasma and recognized ligands with altered nucleotide moiety. Like intrinsic factors, the trout cobalamin-binding protein was present in the stomach and resisted degradation by trypsin and chymotrypsin. It also resembled intrinsic factor in the composition of conserved residues in the primary cobalamin-binding site in the C terminus. The trout cobalamin-binding protein was glycosylated and displayed spectral properties comparable with those of haptocorrin and intrinsic factor. In conclusion, only one soluble cobalamin-binding protein was identified in the rainbow trout, a protein that structurally behaves like an intermediate between the three human cobalamin-binding proteins.

In Silico Analysis of Phosphoproteome Data Suggests a Rich-get-richer Process of Phosphosite Accumulation over Evolution

Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.