The abstracts for the Biocuration 2023 conference have been arranged by the Scientific Program Committee into invited keynote speakers, long talks, short (lightning) talks, and poster presentations. A full conference booklet can be downloaded as a PDF.


Keynote speakers have been invited and are given about 60 minutes to present, including introductions and questions. We suggest limiting your talk to 40-45 minutes and leaving 15 minutes for questions.

Long Talks

Long talks are given 20 minutes to present, including questions. We suggest limiting your talk to 15 minutes and leaving 5 minutes for questions.

Short Talks

Short talks are given 10 minutes to present, including questions. We suggest limiting your talk to 7 minutes and leaving 3 minutes for questions. We recommend your talk is no more than 5 or 6 slides, excluding title, acknowledgements, and other housekeeping.

Poster Presentations

There will be two back-to-back poster presentation sessions for 1 hour each. Even-numbered posters will be presented in the first poster session (Tuesday, April 25th from 16.00-17.00 CEST) and odd-numbered posters swill be presented in the second poster session (Tuesday, April 25th from 17.00-18.00 CEST).

Posters should be printed and brought to the venue by the presenter with a maximum width fof 90cm and height of 100-110cm. Poster boards and materials for hanging posters will be provided. Poster can be hung immediately following registration.

We suggest that posters include a QR code that viewers can scan that either link to a downloadable version of the poster or other relevant resources.

  1. Enhanced integration of UniProtKB/Swiss-Prot, ClinVar and PubMed
    Maria Livia Famiglietti, Anne Estreicher, Lionel Breuza, Alan Bridge, The Uniprot Consortium
    Expand the abstract
    UniProtKB/Swiss-Prot is a reference resource of protein sequences enriched with expert curated information on protein functions, interactions, variation and disease. It describes over 6,200 human diseases linked to over 4,800 protein coding genes and 32,000 disease-associated human variants. Information on variants is annotated by expert curators based on peer-reviewed published articles, identified either manually or thanks to text mining tools such as LitSuggest, a web-based system for identification and triage of relevant articles in PubMed. Our current work focuses on the annotation of clinical significance of variants using the ACMG guidelines and ClinGen tools, submission of interpretations to ClinVar, and annotation of their functional characterization, if available. Functional annotations are standardized using controlled terms from a range of ontologies, including Gene Ontology and VariO to provide UniProt users with machine-readable data. Taken together, this work will increase the coverage and usability of curated variant data in UniProtKB, and the utility of UniProt as a platform to integrate genome and variation data with the knowledge of protein function and disease.
  2. SciBite: A bespoke approach to facilitating FAIR practice in the Life Sciences Industry
    Rachael Huntley
    Expand the abstract
    Pharmaceutical and Biotechnology companies have multiple information-rich documents in various formats and locations, making it difficult to find relevant information using simple search techniques and hampering efforts to make their data FAIR (Findable, Accessible, Interoperable and Reusable). We present an overview of the solutions that the technology company SciBite provides to our customers in the life science space to assist in their journey for FAIR data. SciBite’s technology is reliant on the use of expertly curated ontologies and vocabularies. In addition to creating and updating these ontologies and vocabularies, our curators work with all teams within SciBite, to contribute to product development and software testing, as well as with customers to provide a bespoke solution to their data management. Hopefully this overview will provide biocurators with an insight into a curators’ role in an industry setting.
  3. Enzyme and transporter annotation in UniProtKB using Rhea and ChEBI
    Cristina Casals-Casas, Uniprot Consortium
    Expand the abstract
    The UniProt Knowledgebase (UniProtKB, at is a reference resource of protein sequences and functional annotation. Here we describe a broad ranging biocuration effort, supported by state-of-the-art machine learning methods for literature triage, to describe enzyme and transporter chemistry in UniProtKB using Rhea, an expert curated knowledgebase of biochemical reactions ( based on the ChEBI ontology of small molecules ( This work covers proteins from a broad range of taxonomic groups, including proteins from human, plants, fungi, and microbes, and both primary and secondary metabolites. It provides enhanced links and interoperability with other biological knowledge resources that use the ChEBI ontology and standard chemical structure descriptors, and improved support for applications such as metabolic modeling, metabolomics data analysis and integration, and efforts to predict enzyme function and biosynthetic and bioremediation pathways using advanced machine learning and other approaches.
  4. Assessing Resource Use: A Case Study with the Human Disease Ontology
    J. Allen Baron, Lynn Schriml
    Expand the abstract
    As a genomic resource provider, grappling with getting a handle on how your resource is utilized and being able to document the plethora of use cases is vital to demonstrate sustainability. Herein we describe a flexible workflow built on readily available software, that the Disease Ontology (DO) project has utilized to transition to semi-automated methods to identify uses of the ontology in published literature. The novel R package DO.utils has been devised with a small set of key functions to support our usage workflow in combination with Google Sheets. Use of this workflow has resulted in a three-fold increase in the number of identified publications that use the DO and has provided novel usage insights that offer new research directions and reveal a clearer picture of the DO’s use and scientific impact. Our resource use assessment workflow and the supporting software are designed to be utilized by other genomic resources to achieve similar results.
  5. Genomic Standards Consortium tools for genomic data biocuration - 2023 update
    Lynn Schriml, Chris Hunter, Ramona Walls, Pier Luigi Buttigieg, Anjanette Johnston, Tanja Barrett, Josie Burgin, Jasper Koehorst, Peter Woollard, Montana Smith, Bill Duncan, Mark Miller, Jimena Linares, Sujay Sanjeev Patil
    Expand the abstract
    The Genomic Standards Consortium’s (GSC, successful development and implementation of the Minimum Information about any (x) Sequence (MIxS) genomic metadata standards have established a community-based mechanism for sharing genomic and other sequence data through a common framework. The GSC, an international open-membership working body of over 500 researchers from 15 countries, promotes community-driven efforts for the reuse and analysis of contextual metadata describing the collected sample, the environment and/or the host and sequencing methodologies and technologies. Since 2005, the GSC has deployed genome, metagenome, marker gene, single amplified genome, metagenome-assembled genome and uncultivated virus genome checklists and a library of 23 MIxS environmental packages to enable standardized capture of environmental, human and host-associated study data. In 2022, the GSC relaunched its website using GitHub pages, allowing us to have a shared and distributed approach to website maintenance. We also have made our standards available in the GSC’s GitHub repository ( The latest release, MIxS v6.0, includes six new environmental checklists: Agriculture Microbiome, Host-Parasite Microbiome, Food-animal and animal feed, Food-farm environment, Food-food production facility and Food-human foods. These and other specifications are managed using GitHub releases and LinkML schema tooling and are assigned globally unique URIs and unique MIxS IDs. MIxS is released in excel, JSON-LD, OWL, and SHEX serializations. These standards capture expert knowledge, enable data reuse and integration and foster cross-study data comparisons, thus addressing the critical need for consistent (meta)data representation, data sharing and the promotion of interoperability. The GSC’s suite of MIxS reporting guidelines have been supported for over a decade by the International Nucleotide Sequence Database Collaboration (INSDC) databases, namely NCBI GenBank and BioSample, EMBL-EBI ENA and Biosamples and DDBJ, thus allowing for an enriched environmental and epidemiological description of sequenced samples. To date, over 1,793,966 NCBI BioSample records (compared to 450,000 in 2019) have been annotated with the GSC’s MIxS standards. In the last year, GSC has established two significant collaborations with the National Microbiome data collaborative (NMDC) and Biodiversity Information Standards (TDWG). Upon its launch, NMDC used MIxS v5 for metadata terms that describe and identify biosamples. The NMDC and GSC actively collaborate to improve the MIxS representation. Feedback gathered from the NMDC Data Portal (, users and subject matter experts has identified new metadata terms, updates to improve the MIxS schema and introduced LinkML as a method for managing MIxS, converting the use of MIxS Google Sheets to LinkML’s schemasheets representation, in order to make future versions of MIxS more computable and easier to maintain. To sustainably bridge the GSC standards to recent, biomolecular-focused extensions of TDWG's Darwin Core standard (, we have implemented technical semantic and syntactic mappings in the Simple Standard for Sharing Ontology Mappings (SSSOM). A memorandum of understanding has been formalized to govern this mapping's maintenance, providing users with an authoritative resource for interoperation.
  6. Biocuration at Rhea, the reaction knowledgebase
    Kristian B. Axelsen, Anne Morgat, Elisabeth Coudert, Lucila Aimo, Nevila Hyka-Nouspikel, Parit Bansal, Elisabeth Gasteiger, Arnaud Kerhornou, Teresa Batista Neto, Monica Pozzato, Marie-Claude Blatter, Nicole Redaschi, Alan Bridge
    Expand the abstract
    Rhea ( is a FAIR resource of expert curated biochemical and transport reactions described using the ChEBI ontology of small molecules ( and evidenced by peer-reviewed literature ( Since 2018, Rhea is used for explicit annotation of enzymatic activities in UniProtKB ( It is also used as a reference for enzyme and transporter activity by the GO ontology ( and Reactome ( Rhea covers biochemically characterized reactions from primary and secondary metabolism of a broad range of taxa involving small molecules and the reactive groups of macromolecules. Curation priorities are for a large part driven by reaction requests that mainly comes from UniProtKB. We also create reactions included in the IUBMB Enzyme nomenclature, resulting in Rhea providing full coverage of reactions described by EC numbers. In addition, we also try to create newly characterized reactions of general interest, identified with the help of ML approaches like LitSuggest. To enable the creation of reactions in Rhea, it is very often necessary for Rhea curators to submit the needed compounds to ChEBI, making Rhea one of the primary sources of new compounds. This poster describes the current content of Rhea resource and demonstrates how chemicals, reactions and proteins can be linked between these complementary knowledge resources.
  7. Protein tunnels database
    Anna Špačková, Karel Berka, Václav Bazgier
    Expand the abstract
    Channels in proteins plays significant role in developing new drugs, because of this is important to study these structures. MOLEonline ( ) is available tool for discovering tunnels in proteins structure. Algorithm can find out tunnels, pores and channels on the protein surface. Obtained information can be stored in ChannelsDB database ( Because of the results from algorithm don’t say anything about biological importance, we would like to develop new tool which can recognize it. We are planning based this tool on artificial intelligence together with knowledge of biologically useful channels, and thus create new ontology. This improvement can help with docking molecules into buried active sites and overall in drug discovery.
  8. Machine learning for extraction of biochemical reactions from the scientific literature
    Blanca Cabrera Gil, Anne Morgat, Venkatesh Muthukrishnan, Elisabeth Coudert, Kristian Axelsen, Nicole Redaschi, Lucila Aimo, Alan Bridge
    Expand the abstract
    Rhea ( is an expert curated knowledgebase of biochemical reactions built on the chemical ontology ChEBI (, the reference vocabulary for enzyme and transporter annotation in UniProtKB ( and an ELIXIR Core Data Resource. Rhea currently describes over 15,000 unique reactions and provides annotations for over 23 million proteins in UniProtKB in forms that are FAIR – but most knowledge of enzymes remains locked in literature and is inaccessible to researchers. Machine learning methods provide a powerful tool to address this problem. Here we present work designed to accelerate the expert curation of Rhea - by using Rhea itself to teach large language models the rules of chemistry, and to thereby learn to extract putative enzymatic reactions automatically from the literature. This showcases the power of expert-curated knowledgebases like Rhea to enable the development of machine learning applications.
  9. Application profile based RDF generation for FAIR data publishing
    Nishad Thalhath, Mitsuharu Nagamori, Tetsuo Sakaguchi
    Expand the abstract
    The Resource Description Framework (RDF) is a format for representing information on the Semantic Web, which allows for the publication of data that follows the FAIR (Findable, Accessible, Interoperable, Reusable) principles and can be encoded and expressed with interoperable metadata. RDF has the advantages of being flexible, extensible, and interoperable. Application profiles, also known as metadata application profiles, are a way of modeling and profiling data in RDF. These profiles combine terms from different namespaces and define how they should be used and optimized for a particular local application, along with constraints on their use to ensure the data is valid. Application profiles can promote interoperability between different metadata models and harmonize metadata practices among communities. To ensure the data is FAIR, it is important to define the semantic model of the data, which describes the meaning of entities and relationships in a clear, accurate, and actionable way for a computer to understand. Developing a proper semantic model can be challenging, even for experienced data modellers, and it is important to consider the specific domain and purpose for which the model is being created. Application profiles help ensure the semantic interoperability of the data they represent by providing an explanation of the data and its constraints, and can help FAIRify the data and improve its quality by providing validation schemas. The authors have developed the YAMA Mapping Language (YAMAML) as a tool for creating RDF from non-RDF data. It is based on the Yet Another Metadata Application Profiles (YAMA) format, which is derived from the Description Set Profiles (DSP) language for constructing application profiles. YAMAML is implemented using YAML, a popular data serialization format known for its human readability and compatibility with programming languages. As a variant of JSON, YAML can easily be converted to and from other data formats. YAMAML presents the elements of YAMA's application profiles in a streamlined markup language for mapping non-RDF data to RDF. While YAMAML is intended to generate RDF, it is not itself an RDF representation syntax. The authors have developed a specification and tooling to demonstrate its capabilities as a method for generating RDF. YAMAML can generate RDF from non-RDF data, generate application profiles for the data, and generate RDF validation scripts in Shape Expression Language (ShEx). The authors developed YAMAML with the idea that a proper semantic model can help transform non-FAIR data into linkable data, provide more 5-star open data, and improve its reusability and interoperability.
  10. SwissBioPics – an interactive library of cell images for the visualization of subcellular location data
    Philippe Le Mercier, Jerven Bolleman, Edouard de Castro, Elisabeth Gasteiger, Andrea Auchincloss, Emmanuel Boutet, Lionel Breuza, Cristina Casals Casas, Anne Estreicher, Marc Feuermann, Damien Lieberherr, Catherine Rivoire, Ivo Pedruzzi, Nicole Redaschi, Alan Bridge
    Expand the abstract
    SwissBioPics is a freely available library of interactive high-resolution cell images designed for the visualization of subcellular location data that covers subcellular locations and cell types from all kingdoms of life. The images can be explored on the SwissBioPics website ( and used to display subcellular location annotations on other websites with our reusable web component ( This web component, when provided with an NCBI taxonomy identifier and a list of subcellular location identifiers (UniProt or GO terms), will automatically select the appropriate image and highlight the given subcellular locations. Resources such as UniProt ( and Open Targets ( have adopted SwissBioPics for visualization, and we regularly update the image library as knowledge in UniProt evolves. We hope other developers will adopt the SwissBioPics web component, and would welcome requests to expand the SwissBioPics image library and enhance programmatic access to it. SwissBioPics is freely available under a Creative Commons Attribution 4.0 license (CC BY 4.0).
  11. Glycan biomarker curation for integration with publicly available biomarker and glycobiology resources
    Karina Martinez, Daniel Lyman, Jeet Vora, Nathan Edwards, Rene Ranzinger, Mike Tiemeyer, Raja Mazumder
    Expand the abstract
    Altered glycosylation is associated with almost all major human diseases and reflects changes in cellular status, making glycans a promising target in the search for accessible biomarkers that can indicate disease with high sensitivity and specificity. Despite the translational importance of glycans as biomarkers, there currently appears to be no curation effort which specifically attempts to consolidate and standardize this knowledge. The complexity of glycan structures and heterogeneity of data presents unique challenges for curation efforts. In this exploratory study, we curated 30 glycan, and glycosylation-related, biomarkers from the literature. The curation effort captured glycoconjugates, panels and free glycans, which were then mapped to GlyTouCan, UniProtKB and GlycoMotif accessions. Motifs identified in this study include Type 2 LacNAc, sialyl Lewis x, and Tn antigen, all of which exhibit altered levels of expression associated with disease. Within the context of a larger curation effort including genes, proteins, metabolites, and cells, we developed a data model that accounts for the complex and nuanced nature of glycan biomarkers. The harmonization of glycan biomarker data facilitates integration with an existing biomarker data model and with the glycoinformatic resource, GlyGen. The availability of a curated glycan biomarker dataset will present new opportunities for data mining and disease prediction modeling.
  12. Curating Somatic Variants in Haematological Cancers in COSMIC
    Rachel Lyne, Joanna Argasinska, Denise Carvalho-Silva, Charlotte Cole, Leonie Hodges, Alex Holmes, Amaia Sangrador-Vegas, Sari Ward
    Expand the abstract
    COSMIC, the Catalogue of Somatic Mutations In Cancer (, is the world’s largest source of expert manually curated somatic mutation information relating to human cancers. The most recent release of COSMIC (v97) has focussed on curation of haematological cancers and includes data from whole genome studies, large next generation sequencing panels and case reports of individual cases. Haematological cancers are the fifth most common type of cancer in the world and account for 7% of all cancer deaths. They make up a broad range of cancer types including leukaemias, lymphomas, myelomas and myeloproliferative neoplasms. The haematological cancer focus in COSMIC v97 involved the curation of 76 publications, comprising 43 case studies, 21 research papers and 12 whole exome/genome sequencing studies, which resulted in 2,687 tumour samples with 24,356 novel variants added to the database. Nine new blood tumour types were also added to COSMIC, with seven of these being newly proposed for the National Cancer Institute cancer classification system. This brings the total number of unique forms of haematopoietic & lymphoid neoplasms in COSMIC to over 340. Furthermore, 16 COSMIC tumour types were re-mapped to more specific NCIT tumour types, increasing the precision and interoperability of the data. Research into blood cancers has escalated in recent years and survival rates are much higher than they were thirty years ago. The development of targeted therapies based on mutations in specific genes has played a large part in this success. However, poor publishing practices are hindering data aggregation and sharing, and ultimately are slowing down further development of personalised treatments in this field. From our PubMed search results, 28 publications were not curated because of poor quality or missing data. Haematological cancer publications often publish large sample sets, however it is often difficult or impossible to extract key data points. Such data should be presented in a format that renders itself to computational curation and re-use easily.
  13. Addressing the data challenge of emerging viral diseases: COVID and MPOX resources in ViralZone
    Edouard de Castro, Patrick Masson, Cristina Casals Casas, Arnaud Kerhornou, Chantal Hulo, Ivo Pedruzzi, Sylvain Poux, Nicole Redaschi, Alan Bridge, Philippe Le Mercier
    Expand the abstract
    The emergence of viruses in humans has accelerated, as has the need to monitor and control the resulting new viral diseases. Research and medicine must have access to knowledge and data to develop accurate research, diagnostics, vaccines, and therapeutics. To address this need, dedicated resources for SARS-CoV-2 and monkeypox viruses have been developed in ViralZone. The resources provide curated data on the biology of the virus: genome, transcriptome, proteome, and replication cycle; known antiviral drugs; vaccines; and links to epidemiological data (Nextstrain). For SARS-CoV-2, there is a variant resource with all major circulating variants. Reference sequences are important to the scientific community because they facilitate genome-based diagnostics, bioinformatics, and research by ensuring that all groups use a common sequence. These strains are selected in collaboration with NCBI, Nextstrain, and ViPR to harmonize key viral databases. Modulation of host biology by viruses is a major factor in viral disease. The major host-virus interactions are curated with evidences in the ViralZone resources. For monkeypox virus, 13 of these interactions have been curated into GO-CAM models that can better describe the interactions and their impact on cell biology. We plan to continue to provide specific resources for any emerging or re-emerging viruses in the future.
  14. Rapid Development of Knowledge Bases using Prompt Engineering and GitHub CoPilot Abstract ISB 2023
    Sierra Moxon, Chris Mungall, Harshad Hegde, Mark A. Miller, Nomi Harris, Sujay Patil, Tim Putman, Kevin Schaper, Justin Reese, J. Harry Caufield, Patrick Kalita, Harold Solbrig
    Expand the abstract
    Development of knowledge bases (KBs) and ontologies is a time-consuming and largely manual process, requiring a combination of subject matter expertise and professional curation training. Despite recent advances in fields such as natural language processing and deep learning, most trusted knowledge in such repositories is either manually entered, or generated by deductive reasoning or rule-based processes. There are a number of reasons why AI techniques are not yet mainstream in KB construction[5,6]. One of the main obstacles is the trustworthiness of predicted facts. While these tools often do well against common ranking metrics, a significant portion of predicted facts are wrong[3], and incorporating them would pollute the KB and result in the erosion of trust in that resource. Instead of using AI in isolation and forcing a review of its predicted facts by a subject matter expert, a better approach would be to draw from the strengths of each member of the partnership working in tandem. The expert has wide-ranging and deep domain knowledge, the ability to understand at a fundamental level natural language descriptions of phenomena (e.g. as described in the scientific literature), and the ability to reason about representations of these phenomena. In contrast, the AI has no such knowledge or understanding but does possess a phenomenal ability to pattern-match and consume vast troves of information at a superficial level. Increasingly, AIs can also generate plausible and potentially correct content. Here we present initial findings on content generation assistance via prompt engineering. We call this approach Knowledge Base Prompt Engineering (KBPE). In this approach, large foundational language models intended for assisting in software development are adapted to KB curation workflows through the use of schemas structured using the Linked Data Modeling Language (LinkML) framework[9]. We find that software-based prompt engineering tools (specifically PyCharm[7] and GitHub Copilot[8]) work surprisingly well for a subset of knowledge acquisition tasks, in particular for rote tasks involving the structuring of common knowledge. A particularly relevant finding is that this approach readily adapts to custom domain-specific schemas, and is easily primed by previously stated facts. Results are highly variable and dependent on multiple dynamic factors, but in some cases, a single prompt can generate hundreds of largely accurate and useful facts, representing speedups of orders of magnitude. The challenge here is to identify and discard true-looking facts that are inaccurate. Due to the wide range of knowledge bases, the time that is taken to construct them, and the challenges in evaluating them, we performed a qualitative assessment in order to broadly inform us of the general feasibility of knowledge-based prompt engineering. Our tests include a variety of assessments including lexical definition completion, fact completion, fact negation and validity checking, logical definition completion, classification of facts, and the auto-generation of data properties. Using gene ontology curation[1,2,4] as an example, we walk through our findings in this presentation.
  15. Capturing the experimental research history of signalling pathways in Drosophila melanogaster
    Giulia Antonazzo, Helen Attrill, Nicholas H. Brown, Flybase Consortium
    Expand the abstract
    FlyBase, the knowledgebase for Drosophila melanogaster, introduced a new signalling pathways resource in 2018. This resource systematically assembles the experimental knowledge on key signalling pathways, providing research evidence-based lists of core members and regulators, network visualizations of pathway component physical interactions, as well as integrating tools and data to aid bench research. Since its introduction, this resource has been continuously updated to integrate more pathways as well as new features, including thumbnail images that show a textbook pathway representation, and a graphical comparison of member gene functions. We describe the growth of the resource and demonstrate its potential and utility to the research community. We use the corpus of highly curated pathway data to analyse the knowledge landscape of Drosophila signalling pathway research and ask questions such as, which genes are most studied? How has the volume of research on certain pathways changed over the years? We also present analyses that make use of the curated pathway members lists, together with functional genomics data available elsewhere in FlyBase, to exemplify how the resource can contribute to characterizing the biological properties of signalling pathways in Drosophila.
  16. GlyGen: Computational and Informatics Resources for Glycoscience
    Rene Ranzinger, Karina Martinez, Jeet Vora, Sujeet Kulkarni, Robel Kahsay, Nathan Edwards, Raja Mazumder, Michael Tiemeyer
    Expand the abstract
    Advancing our understanding of the roles that glycosylation plays in development and disease is frequently hindered by the diversity of the data that must be integrated to gain insight into these complex phenomena. GlyGen is an initiative with the goal of democratizing glycoscience research by developing and implementing a data repository that integrates diverse types of information, including glycan structures, glycan biosynthesis enzymes, glycoproteins, genomic and proteomic knowledge. To achieve this integration, GlyGen has established international collaborations with database providers from different domains (including but not limited to EBI, NCBI, PDB, and GlyTouCan) and glycoscience researchers. Information from these resources and groups are standardized and cross-linked to allow queries across multiple domains. To facilitate easy access to this information, an intuitive, web-based interface ( has been developed to visually represent the data. In addition to the browser-based interface GlyGen also offers RESTful webservice-based APIs and a SPARQL endpoint, allowing programmatic access to integrated datasets.    For each glycan and glycoprotein in the dataset, GlyGen provides a details page that displays information from the integrated resources in a concise representation. Individual details pages are interlinked with each other allowing easy data exploration across multiple domains. For example, users can browse from the webpage of a glycosylated protein to the glycan structures, that have been described to be attached to this protein, and from there, to other proteins that carry the same glycan. All information accessed through GlyGen is linked back to original data sources, allowing users to easily access and browse through information pages in these resources as well. The GlyGen portal itself provides multiple different search interfaces for users to find glycans and proteins based on their properties or annotations. The most advanced version of these searches is the GlyGen Super Search that visualizes the entire data model in one graph and enables users to find glycans and proteins by adding constrains to this graph. Beyond the data on glycans and proteins, GlyGen also provides multiple tools for studying glycosylation pathways, investigating relationships between glycans based on incomplete structures or mapping of different ID namespace.   Our goal is to provide scientists with an easy way to access the complex information that describes the biology of glycans and glycoproteins. To schedule an individual demo of GlyGen or add your data to GlyGen contact Rene Ranzinger (
  17. Using Disease Focused Curation to Enhance Cross Species Translation of Phenotype Data
    Susan M. Bello, Yvonne M. Bradford, Monte Westerfield, Cynthia L Smith, The Mgi And Zfin Curation Teams
    Expand the abstract
    Using model organism phenotype data to improve human disease diagnosis and treatment is limited by difficulties in translating model organism phenotypes to patient signs and symptoms and by incomplete curation of model organism data. Ontologies, like the Human Phenotype (HPO) ontology which is used to annotate KidsFirst data, Mammalian Phenotype (MP) ontology used for mouse phenotypes, and the Phenotype and Trait Ontology (PATO) with the Zebrafish Anatomy ontology (ZFA) used for zebrafish phenotypes, have been developed to standardize reporting of phenotype data in each species, but translating the data among species is not always straightforward without defined relations between these ontologies. Mouse Genome Informatics (MGI, and the Zebrafish Information Network (ZFIN, have established a joint effort to help bridge the translation gap through focused curation of model organism research on diseases from the KidsFirst ( data resource. Both groups identified relevant publications for models of Scoliosis and Cleft Palate and annotated all phenotypes reported for these models. As part of phenotype curation workflows, terms missing from the relevant ontologies were identified and added. After annotation, the set of mouse model phenotypes were extracted from MGI and mapped to HPO terms using the Simple Standard for Sharing Ontology Mappings (SSSOM). These mappings are available from the Mouse-Human Ontology Mapping Initiative GitHub repository ( Mapped MP and HPO terms are also used to improve alignment with ZFIN annotations. We are collaborating with the KidsFirst team to incorporate these data into the KidsFirst data portal. The expanded annotations and curated mappings will both enrich data available for model to patient translations and support development of new methods to improve phenotype translations in general. Supported by NIH grant OD033657.
  18. Toward a curated glyco-interactome knowledgebase for the biology community using CarbArrayART
    Yukie Akune, Sena Arpinar, René Ranzinger, Ten Feizi, Yan Liu
    Expand the abstract
    Glycans are chains of variously linked monosaccharides biosynthesized by glycosyltransferases. They occur as oligosaccharides, and parts of glycoconjugates, such as polysaccharides, glycoproteins and glycolipids. They participate in innumerable recognition systems in health (development, cell differentiation, signalling and immunomodulation) and in disease states (inflammatory, infectious and non-infectious including neoplasia). Glycan microarray technologies for sequence-defined glycans, first introduced in 2002 by Feizi and colleagues, have revolutionized the molecular dissection of specificities of glycan-protein interactions [1-3]. Many datasets have been published in almost 1700 scientific publications from groups using different glycan array platforms. However, the data interpretation is not always straightforward as there are differences in array platforms which may give differing results with the same glycan binding system. The glycans are variously derivatized using numerous linkers and chemistries so that they can be immobilized on microarray surfaces covalently or noncovalently. These parameters can have a pronounced effect on the microarray readouts, hence the need for curation and annotation in the interpretation of glycan array data. Software tools have facilitated data handling [4,5] and we have recently released an advanced and distributable software called Carbohydrate micro-Array Analysis and Reporting Tool (CarbArrayART, for glycan array data processing, storage and management [6]. As part of GlyGen ( [7], a data integration and dissemination project for carbohydrate- and glycoconjugate-related data, we have been involved in the planning and design of a much-needed public glycan array data repository. An extension of CarbArrayART is being developed to allow uploading and downloading data to and from the repository. A critical component of this submission system is the definition and implementation of a common format for glycan array data and associated metadata in accordance with the glycan array minimum information guidelines developed by the MIRAGE commission [8]. In this communication, we will share our progress in defining criteria for glycan microarray data curation. These include establishing glycan microarray metadata standards for describing glycan binding samples, experimental conditions, glycan probes arrayed, and microarray data processed. Using these standards, we have curated published array data from the Glycosciences Laboratory for submission to the GlyGen glycan microarray repository. In the future, CarbArrayART will serve as the vehicle for data transfer between local databases and the public glycan array repository, not only for newly generated data but also for datasets from existing research publications. We will extend our criteria to define glycan ‘recognition motif(s) for each glycan binding system. This will fill knowledge gaps in glycan-mediated molecular interactions in the wider biological landscape. 1. Fukui S, Feizi T, et al. Nat.Biotechnol. 20:1011-7 (2002) 2. Rillahan CD, Paulson JC. Annu.Rev.Biochem. 80:797-823 (2011) 3. Palma AS, Feizi T, et al. Curr.Opin.Chem.Biol, 18:87-94 (2014) 4. Stoll M, Feizi T. Proceedings of the Beilstein Symposium on Glyco-Bioinformatics. 123-140 (2009) 5. Mehta AY, Heimburg-Molinaro J, et al. Beilstein J.Org.Chem. 16:2260-2271 (2020) 6. Akune Y, Arpinar S, et al. Glycobiology. 32:552-555 (2022) 7. York WS, Mazumder R, et al. Glycobiology. 30:72-73 (2020) 8. Liu Y, McBride R, et al. Glycobiology. 27:280-284 (2017)
  19. Exploiting single-cell RNA sequencing data on FlyBase
    Damien Goutte-Gattat, Nancy George, Irene Papatheodorou, Nick Brown
    Expand the abstract
    Single-cell RNA sequencing has proved an invaluable tool in biomedical research. The ability to survey the transcriptome of individual cells offers many opportunities and has already paved the way to many discoveries in both basic and clinical research. For the fruit fly alone, nearly a hundred of single-cell RNA sequencing datasets have already been published since the first reported use of the technique in fly laboratories in 2017, a number that is only expected to grow quickly in the coming years. This increasing amount of single-cell transcriptomic data available, including whole-organism single-cell transcriptomic atlases, creates a challenge for biological databases to integrate these data and make them easily accessible to their users. FlyBase is the Model Organism Database (MOD) for all data related to Drosophila melanogaster. It provides access to a wide range of scientific information either manually curated from the published literature or from high-throughput research projects. For single-cell RNA sequencing data, we aim to help fly researchers to: (i) discover the available Drosophila datasets; (ii) learn the most important informations about a dataset of interest; and (iii) get a quick overview of the expression data from those datasets. To that end, we have set up a collaboration with the Single Cell Expression Atlas (SCEA), the EMBL-EBI resource for gene expression at the single cell level. FlyBase curators assist the EMBL-EBI’s data scientists in obtaining and annotating Drosophila single-cell RNA sequencing datasets; in return, the SCEA provides FlyBase with the processed data in a standardized format, allowing for easier ingestion into our database. We then exploit the ingested data to enrich our gene report pages with specific displays for single cell expression data, giving our users an immediate view of the cell types in which a given has been found to be expressed.
  20. Biocuration meets Deep Learning
    Gregory Butler
    Expand the abstract
    Machine learning (ML) for protein sequence analysis relies on well-curated sources such as Swiss-Prot to provide ``gold standard'' datasets to train, test and evaluate tools that classify unknown proteins. Most tools use supervised learning where the labels (annotations) from curators are essential. The trained classifiers may become basic tools in regular use by curators, thus completing the circle from curation to ML to curation. The bias in our understanding of cell molecular biology is reflected in the resources, through no fault of the curators. The accumulation of knowledge is driven by many factors, including ease of lab work, available instruments, availability of funding, historical focus on model organisms, and an emphasis on the publication of ``positive'' results rather than ``negative'' results. A lack of negative results effectively means that ML is not performing the expected positive-negative discrimination of samples but rather positive-unknown discrimination. Furthermore, most proteins are not fully annotated, that is, not all roles of the protein are known, only those reported in the literature. This means that multi-label ML --- the ideal where each of the roles is predicted --- is rarely attempted. Indeed proteins annotated with multiple roles are often removed from the gold standard datasets for binary or multi-class learning. Classical ML is highly dependent on feature engineering (FE) to determine a good set of features (attributes) on which to base the classifier. FE is difficult as the feature space is virtually limitless. Deep learning (DL) offers tools to bypass FE. The models themselves learn (sub)features at each level of the DL architecture. The trade-off requires substantial computational resources to train the DL models, and also large datasets. DL models, like many classical ML models, lack interpretability, so predictions cannot be explained to the end-user scientists. AlphaFold attracted wide coverage in the literature for its success in the CASP challenge in 2018. Training required 170,000 proteins with structures and required a few weeks using 100-200 GPUs. Significantly this led to the AlphaFold Protein Structure Database, with predicted structures for the proteomes of model organisms. Beyond structure, computational biologists are utilizing protein language models (PLM) from deep learning. A PLM is constructed by self-supervised learning which requires no labels, though they do require training on a very large number of sequences, which does require substantial computing resources. The ProtBERT-BFD PLM was trained on BFD with 2.5 billion sequences, which took days using 1024 TPUs (tensor processing units). Initial results are promising for subcellular localization. We are also having state-of-the-art results applying PLM to a broad range of classification tasks for membrane proteins. While interpretability remains a major obstacle, DL representations address the positive-unknown problem. Furthermore, so-called few-shot learning, which uses mappings between DL representation from different sources such as sequence, structure, annotations, and text descriptions, allows predictions in situations where there is a small number of labelled examples (even zero examples). How DL will impact the work of curators remains to be seen.
  21. AlphaFold structure predictions help improve Pfam models and annotations - Usage in curation
    Sara Chuguransky, Typhaine Paysan-Lafosse, Alex Bateman
    Expand the abstract
    AlphaFold DB is an online resource developed by DeepMind in collaboration with the EMBL-EBI, based on results of AlphaFold 2.0, an AI system that predicts highly accurate 3D protein structures from the amino acid sequence. The latest version of AlphaFold DB contains structure predictions of the human proteome and 47 other key organisms, covering most of the representative sequences from the UniRef90 data. The availability of experimental 3D structures is limited, therefore, AlphaFold predictions are really helpful in curation to provide more informative and accurate annotations, especially for poorly characterised sequences. In Pfam, a protein classification database based at the EMBL-EBI widely used by the scientific community, we have historically used experimentally determined, when available, to refine domain boundaries, improve our models, protein coverage and annotations. In addition, we are able to find relationships with other Pfam entries and group them in superfamilies, that we call clans, given structural similarities that may imply a common origin. We have now started to use AlphaFold predictions to revisit existing families that have never had an experimentally determined structure to correct their domain boundaries and find their evolutionary relationships. We also build new domains currently missing in Pfam based on these highly accurate structure predictions. This curation effort is supported by AlphaFold colab and Foldseek tools, which assist us in determining clan memberships. Here, we present some examples of improved annotations generated using these tools. The human protein ZSWIM3 currently has two domain annotations, a zinc finger domain (SWIM-type, residues 531-572) and a domain of unknown function (579-660). AlphaFold predicts the presence of 5 domains, which according to the pLDDT score, are highly accurate. Therefore, we built new domains: PF21599, N-terminal domain (1-104, CL0274), PF21056, a RNaseH-like domain (179-304, CL0219) and PF21600, and helical domain (312-437). For PF21599 and PF21056, we were able to find structural relationships with WRKY-like DNA-binding domains and RNaseH domains, respectively, using the AlphaFold colab tool [3]. Similarly, we improved the domain boundaries for the C-terminal DUF (PF19286): It previously covered 579-660 region of the protein, which, according to the structure prediction, was longer than it should and partially overlapped the zinc finger domain. We adjusted this boundaries to cover the two α-helices. In this other example, we refined the model of this uncharacterised protein from a fruit fly, A0A034WDA7 which is is annotated as PF05444 - Protein of unknown function (DUF753). From structure prediction, we can see that this protein actually consists of two identical domains. Based on this, we splitted this Pfam entry into the corresponding domains. We conclude that all these tools are very helpful not only for curation but also for research, as they help us to improve protein classification and provide better and more accurate predictions. This is particularly useful in those cases for which functional or structure information is scarce.
  22. Protein Structures and their cross-referencing in UniProt
    Nidhi Tyagi, Uniprot Consortium
    Expand the abstract
    Annotation of proteins from structure-based analyses is an integral component of the UniProt Knowledgebase (UniProtKB). There are nearly 200,000 experimentally determined 3-dimensional structures of proteins deposited in the Protein Data Bank. UniProt works closely with the Protein Databank in Europe (PDBe) to map these 3D structural entries to the corresponding UniProtKB entries based on comprehensive sequence and structure-based analyses, to ensure that there is a UniProtKB record for each relevant PDB record and to import additional data such as ligand-binding sites from PDB to UniProtKB. SIFTS (Structure Integration with Function, Taxonomy and Sequences), which is a collaboration between the Protein Data Bank in Europe (PDBe) and UniProt, facilitates the link between the structural and sequence features of proteins by providing correspondence at the level of amino acid residues. A pipeline combining manual and automated processes for maintaining up-to-date cross-reference information has been developed and is run with every weekly PDB release. Various criteria are considered to cross-reference PDB and UniProtKB entries such as a) the degree of sequence identity (>90%) b) an exact taxonomic match (at the level of species, subspecies and specific strains for lower organisms) (c) preferential mapping to a curated SwissProt entry (if one exists) or (d) mapping to proteins from Reference/Complete proteome or (e) mapping to the longest protein sequence expressed by the gene. Complex cases are inspected manually by a UniProt biocurator using a dedicated curation interface to ensure accurate cross-referencing. These cases include short peptides, chimeras, synthetic constructs and de novo designed polymers. The SIFTS initiative also provides up to date cross referencing of structural entries to literature (PubMed), taxonomy (NCBI), Enzyme database (IntEnz), Gene Ontology annotations (GO), protein family classification databases (InterPro, Pfam, SCOP and CATH). In addition to maintaining accurate mappings between UniProtKB and PDB, a pipeline has been developed to automatically import data from PDB to enhance the unreviewed records in UniProtKB/TrEMBL. This includes details of residues involved in the binding of biologically relevant molecules including substrate, nucleotides, metals, drugs, carbohydrates and post-translational modifications that greatly improves the biological content of these records. UniProt has successfully completed the non-trivial and labour intensive exercise of cross referencing 187,997 PDB entries (647,078 polypeptide chains) to 60,599 UniProtKB entries (manual curation). Manual annotation of protein entries with 3D-structures is given high priority and such proteins are curated based on relevant literature. UniProt also provides structural predictions through AlphaFold for various proteomes. All this work enables non-expert users to see protein entries in the light of relevant biological context such as metabolic pathways, genetic information, molecular functions, conserved motifs and interactions etc. Structural information in UniProtKB serves as a vital dataset for various academic and biomedical research projects.
  23. YeastPathways at the Saccharomyces Genome Database: Transitioning to Noctua
    Suzi Aleksander, Dustin Ebert, Stacia Engel, Edith Wong, Paul Thomas, Mike Cherry, Sgd Project
    Expand the abstract
    The Saccharomyces Genome Database (SGD, is the leading knowledgebase for Saccharomyces cerevisiae. SGD collects, organizes and presents biological information about the genes and proteins of the budding yeast, including information concerning metabolism and associated biochemical pathways. The yeast biochemical pathways were originally sourced from the YeastCyc Pathway/Genome Database, which uses the metabolic pathways from MetaCyc. YeastCyc Pathways were imported into SGD in 2002, then SGD biocurators edited them as necessary to make them specific to S. cerevisiae, using publications from the primary literature. This comprehensive curation ensured that only the reaction directions and pathways that are physiologically relevant to S. cerevisiae were included, and also provided written summaries for each pathway. Loading, editing, and maintaining YeastPathways displayed at SGD was accomplished using the Pathway Tools software. YeastPathways have now been available at SGD for over 20 years, with the last major content update in 2019. Recently, YeastPathways have been transitioned from a curation system that uses Pathway Tools software to one that uses the Gene Ontology curation platform Noctua, the interface SGD already uses to curate GO annotations. Each of the 220 existing pathways in YeastPathways has been converted from Pathway Tools BioPAX format into separate GO-Causal Activity Models (GO-CAMs), which are available as Turtle (.ttl) files. The GO-CAM structured framework allows multiple GO annotations to be linked, which is ideal for a metabolic pathway. Although many of the steps are automated, manual intervention was required throughout the process to complete the GO-CAMs and to verify inferences made by the conversion tools. Using the Noctua curation interface for biochemical pathways, without the need for external software, will streamline curation. Additionally, each model’s metadata is accessible to any interested party. GO-CAMs can be deconstructed into standard GO annotations, making the pathway information accessible for enrichment studies and other applications. YeastPathways as GO-CAMs will also be available through the Gene Ontology’s GO-CAM browser and the Alliance of Genome Resources, as well as any other resources that display GO-CAMs. This upgrade to the SGD’s curation process will also make YeastPathways more transparent and compliant with FAIR guidelines and TRUST principles.
  24. Adding semantics to data digitization: strengths and possibilities
    Pratibha Gour
    Expand the abstract
    Over the years, life sciences has changed from descriptive to a data-driven discipline wherein, new knowledge is produced at an ever increasing speed and thus, the list of research articles, databases and other knowledge resources keep on piling up. These data become knowledge within a defined context only when the relations among various data elements are understood. Thus, discovery of novel findings depends a lot on the integration, comparison and interpretation of these massive data sets. The volume as well as variability of data, both poses a challenge to its seamless integration. This problem is even intensified in case of low-throughput (better called gold standard) data published in research articles, as no uniform/structured formats are available for their storage and dissemination. These experimental data are usually represented as a gel image, autoradiograph, bar graph etc. Moreover, experimental design is highly complex and diverse such that even studies using same experimental technique can have a very different design. Manually curated database of rice proteins (MCDRP) addresses these issues by adopting data models based on various ontology terms or custom-made notations, which index experimental data itself, such that it becomes amenable for automated search. This semantic integration not only renders the experimental data suitable for computer-based analysis such as rapid search and automated interpretation but also provides it a natural connectivity. Some of the most interesting correlations could be drawn by analyzing proteins that share a common ‘Trait’ or ‘Biological Process’ or ‘Molecular Function’. Moreover, such a semantic digitization facilitates searching of/access to extensive experimental data sets at granular level. The data digitization formats used here are generic in nature and facilitate digitization of almost every aspect of the experimental data, thereby providing a better understanding of any biological system. These models have been successfully used to digitize data from over 20,000 experiments spanning over 500 research articles on rice biology.
  25. Swiss Personalized Health Network: Making health data FAIR in Switzerland
    Deepak Unni, Sabine Österle, Katrin Crameri
    Expand the abstract
    The Swiss Personalized Health Network (SPHN) is a national initiative responsible for the development, implementation, and validation of coordinated data infrastructures that make health-related data FAIR (Findable, Accessible, Interoperable, Reusable) and available for research in Switzerland in a legally and ethically compliant manner. SPHN brings together stakeholders from various university hospitals and research institutions across Switzerland to enable the secondary use of health-related data, including but not limited to clinical routine data, omics and cohort data for personalized health research. To that end, the SPHN initiative consists of several key elements. Firstly, SPHN provides an Interoperability Framework for the definition and harmonization of health data semantics. Therein, the various health-related concepts and attributes are defined, with meaning binding to internationally recognized terminologies like SNOMED-CT and LOINC, and for certain attributes, the value sets defined from both international and local terminologies. The semantics are then translated into a formal representation - using Resource Description Framework (RDF), RDF-Schema (RDFS), and Web Ontology Language (OWL) - to create the SPHN RDF Schema. Secondly, SPHN provides an ecosystem that supports the generation, quality check, dissemination, and analysis of health data. The system enables the translation of health data into RDF, provides a terminology service for access to external terminologies in RDF, and a schema template to support projects to create and work with their individual subset of concepts and attributes. Since both the schema and the data are in RDF, the ecosystem relies heavily on other semantic web technologies like SPARQL Protocol and RDF Query Language (SPARQL), and Shapes Constraint Language (SHACL). For example, the ecosystem includes tools for performing quality checks and improving the quality of the data represented in RDF, such as the SPARQL Generator (SPARQLer) and SHACL Generator (SHACLer). By building on top of well-established standards, the technical burden on hospitals and projects can be reduced. One such example is the SPHN Connector, a tool for connecting data providers to SPHN infrastructure and services, which provides a unified interface for ingesting data in various formats and handles conversion to SPHN compliant RDF data and validation. On BioMedIT, Switzerland’s secure trusted research environment for processing sensitive data, researchers find the tools and support to work with their sensitive graph data. Lastly, SPHN also provides services that improve the discoverability of data, with the SPHN Federated Query System (FQS), and of metadata, contributing with Swiss cohort data to the international Maelstrom Catalogue, enabling researchers to explore and identify cohorts of interest and request access to the data. Since 2017, SPHN has contributed to the establishment of clinical data management platforms at the five Swiss University Hospitals to make health data efficiently available for research, has supported researchers in the discovery and analysis of data, and built the required infrastructures to bridge the gap between healthcare and data-driven research. Moving forward, the SPHN is exploring approaches for the translation of data in SPHN into data models like OMOP, i2b2, and FHIR to increase interoperability with existing national and international communities.
  26. From Model Organisms To Human: Increasing Our Understanding Of Molecular Disease Mechanisms By Data Curation
    Yvonne Lussi, Elena Speretta, Kate Warner, Michele Magrane, Sandra Orchard, Uniprot Consortium
    Expand the abstract
    The UniProt Knowledgebase (UniProtKB) is a leading resource of protein information, providing the research community with a comprehensive, high quality and freely accessible platform of protein sequences and functional information. Manual curation of a protein entry includes sequence analysis, annotation of functional information from literature, and the identification of orthologs. For human entries, we also provide information on disease involvement, including the annotation of genetic variants associated with a human disease. The information is extracted from scientific literature and from the OMIM (Online Mendelian Inheritance in Man) database. Identifying the underlying genetic variation and its functional consequences in a disease is essential for understanding disease mechanisms. Model organism research is integral to unraveling the molecular mechanisms of human disease. Therefore, information of human proteins involved in disease is supplemented with data from protein orthologs in model organisms, providing additional information based on mutagenesis assays and disruption phenotypes. With these efforts, we hope to improve our understanding between the association of genetic variation with its functional consequences on proteins and disease development. Our ongoing efforts aim to compile available information on proteins associated with human diseases, including the annotation of protein variants and the curation of orthologs in model organisms, to help researchers to better understand the relationship between protein function and disease. Understanding disease mechanisms and underlying molecular defects is crucial for the development of targeted therapy and new medicines.
  27. Beyond advanced search: literature search for biocuration
    Matt Jeffryes, Henning Hermjakob, Melissa Harrison
    Expand the abstract
    Biocurators are experts at identifying papers of interest within the vast scientific literature. Workflows for biocuration are very diverse, but most involve the use of literature search engines, such as PubMed, Europe PMC or Google Scholar. However, these search engines are designed for a general scientific audience. We have developed a ‘biocuration toolkit’ which incorporates search features specific to biocuration, with flexibility to fit within a variety of biocuration workflows. An innovative feature is the capability to exclude current content of a database from result sets, allowing to focus on increased coverage of a resource rather than adding additional evidence for already well-covered facts. We have developed our tool in collaboration with IntAct database biocurators. The IntAct database contains evidence for the interaction of pairs of molecules. The specific biocuration task which IntAct wish to improve their performance on is to curate molecular interactions into their database where at least one of the pair has few or no existing entries within the database, and particularly those where the molecule has entries in peer databases. Our tool allows biocurators to prioritise literature which mentions proteins which are not yet present in the IntAct database. However, this feature is designed flexibly to allow filtering based on other axes such as disease or organism mentions. As we gather feedback on biocurators we intend to implement further filtering and prioritisation methods, which may be combined in a modular way by biocurators to direct their searches, alongside typical advanced search features such as boolean search terms, filtering by date and article type. By building on top of Europe PMC, we are also able to optionally support searching of preprint articles from a number of servers, including bioRxiv and medRxiv.
  28. Curation and integration of single-cell RNA-Seq data for meta-analysis
    Jana Sponarova, Pavel Honsa, Klara Ruppova, Philip Zimmermann
    Expand the abstract
    The immune system is a complex system of various cell populations and soluble mediators, and its proper understanding requires detailed analysis. These efforts have been boosted by the development of single-cell RNA sequencing (scRNAseq). ScRNAseq is a powerful approach to understanding molecular mechanisms of development and disease and uncovering cellular heterogeneity in normal and diseased tissues, but it possesses several challenges. As the amount of scRNAseq data present in the public domain has grown substantially in the last few years, a platform is needed to enable the meta-analysis. Moreover, multiple different scRNAseq protocols are in use, and a number of additional modalities co-analyzed with RNA is growing. Another challenging aspect of the analysis of scRNAseq data is the correct cell type identification which is especially important for studying the immune system consisting of a wide range of cell types and states that might have very different functions. Here we present a custom pipeline that enables unified and standardized processing of single-cell transcriptomics data from various sources, utilizing different protocols (10x, Smart-Seq) and accompanied by additional modalities (VDJ-Seq, CITE-Seq, Perturb-Seq). The processing of every single study includes raw data mapping, standardized and strict quality control, data normalization, and integration. These steps are followed by cell clustering with subsequent cluster identification and description. The cell type annotation is then synchronized across all studies in the compendium. This approach makes the cell type annotation more accurate and enables a compendium-wide meta-analysis. To keep accuracy yet gain efficiency in the annotation process, we have built multiple proprietary cell atlases and references. The pipeline outputs are enriched with sample-level information (e.g., patient-level data), and data are integrated into a user-friendly analysis software - GENEVESTIGATOR, a high-performance visualization tool for gene expression data for downstream analysis on single-cell as well as pseudo-bulk level. In summary, we have built a manually curated and globally normalized scRNAseq compendium mainly consisting of immune cells obtained from studies focused on immuno-oncology, autoimmune diseases, and other therapeutic areas. This deeply harmonized compendium represents an important asset for downstream ML and AI applications in pre-clinical biomarker discovery and validation.
  29. The Nebion Cell-type Ontology
    Anna Dostalova, Pavel Honsa, Jana Sponarova
    Expand the abstract
    The advent of single-cell profiling has accelerated our understanding of the cellular composition of human body, however, it has also brought a challenge of correct classification and description of the various cell types and cell states. In an effort to consolidate annotations that are featured in our biomarker discovery platform, GENEVESTIGATOR, we have built a comprehensive ontology of approx. 700 cell-types. It has a simplified, easy-to-navigate tree structure, where each cell type is present only once. The cell-type categories were compiled from several sources and structured by their developmental lineages, function, and phenotypes. The same ontology is used for annotating human, mouse, and rat studies. In combination with two other ontologies (“Tissue” and “Cell state”), it enables streamlined analyses of single-cell sequencing data from across different studies and research areas. The presented ontology helps to advance our understanding of the complexity of cell types.
  30. Curated information about single-cell RNA-seq protocols
    Sagane Joye, Anne Niknejad, Marc Robinson-Rechavi, Julien Wollbrett, Sebastien Moretti, Tarcisio Mendes De Farias, Marianna Tzivanopoulou, Frédéric Bastian
    Expand the abstract
    Single-cell RNA sequencing (scRNA-seq) technologies have dramatically revolutionized the field of transcriptomics, enabling researchers to study gene expression at the single cell level and uncover new insights in a variety of fields. However, the rapid pace of development in this field means that new scRNA-seq protocols are being developed and modified on an ongoing basis, each with its own unique characteristics and varying levels of performance, sensitivity, and precision. In addition to the technical differences between scRNA-seq protocols, the way in which the data is processed can also vary depending on the chosen method and on the study. This diversity of scRNA-seq protocols and the associated differences in processing requirements can make it difficult for researchers to determine the most suitable method for their specific needs and to correctly process the resulting data, and for databases such as Bgee to integrate these data. To help navigate this complex landscape, we have created an exhaustive table that includes essential information about 23 protocols, including isolation methods, exact barcode structure, target RNA types, transcript coverage, multiplexing capability, strand specificity, amplification and reverse transcriptase strategies. In addition, we provide brief guidelines for best practices to apply depending on the chosen technology. We hope that this summary will be a valuable resource for scientists looking to easily find and compare key information on the different scRNA-seq protocols, allowing them to make informed decisions about which method to use and how to correctly process their data.
  31. Collaborative Annotation of Proteins Relevant to the Adaptive Immune Response
    Randi Vita, Nina Blazeska, Hongzhan Huang, Daniel Marrama, Karen Ross, Cathy H Wu, Maria Martin, Bjoern Peters, Darren A Natale
    Expand the abstract
    The Immune Epitope Database (IEDB) is a freely available resource funded by the National Institute of Allergy and Infectious Diseases (NIAID) that has cataloged experimental data on the adaptive immune response to more than 1.5 million antibody and T cell epitopes studied in humans, non-human primates, and other animal species in the context of infectious disease, allergy, autoimmunity, and transplantation. Epitopes are the portions of an antigen, frequently a protein, that are recognized by antibodies and T cell receptors. Epitopes are key to understanding healthy and abnormal immune responses and are crucial in the development of vaccines and therapeutics. We have undertaken a collaboration to connect the epitope data in the IEDB to the rich functional annotation of proteins in UniProtKB, to the explicit representation of proteoforms in the Protein Ontology (PRO), and to the library of post-translational modifications (PTMs) available in iPTMnet. We are also combining IEDB data with resources specializing in protein-protein interactions, human genetic variation, diseases, and drugs. Integration of the IEDB’s immune epitope information with the wealth of additional biomedical data in humans and model organisms will enable novel opportunities for hypothesis generation and discovery. Researchers interested in human disease will be able to fully exploit knowledge derived from model organisms, improve disease models in non-human organisms, and identify potential cross-reactivities within and across organisms.This will enable novel queries of high interest to translational researchers. For example, what PTMs and/or genetic variants overlap with an epitope of interest or if a human epitope of interest is found in orthologous/homologous proteins in model organisms or vice-versa. Answers to such questions can provide insight into factors that affect auto-antigenicity or immune evasion by pathogens. Display of this information in the UniProt ProtVista environment and via the IEDB website will make it easily accessible to the large community of immunology and disease researchers. This collaborative effort among multiple major resources will overcome barriers to consumption of IEDB data and enrich the knowledge available in UniProtKB, PRO, and iPTMnet, thereby supporting inquiry into the role of the immune system in human disease.
  32. Retrieval of expert curated transcriptomics metadata through a user-friendly interface
    Anne Niknejad, Julien Wollbrett, Marc Robinson-Rechavi, Frederic B. Bastian
    Expand the abstract
    Transcriptomics data are of major importance to understand organism biology, and are being made available notably through primary repositories hosting their raw data, such as the Sequence Read Archive, European Nucleotide Archive, or DDBJ Sequence Read Archive. These repositories are essential for allowing for reproducible data science, but usage is limited by the available metadata. These metadata are often free text as directly provided by the authors of an experiment, cannot be checked for inconsistencies because of the amount of data submitted daily, and cannot be queried using, e.g., ontology reasoning. Bgee ( is a database for retrieval and comparison of gene expression patterns and levels across multiple animal species, produced from multiple data types (bulk RNA-Seq, single-cell RNA-Seq, Affymetrix, in situ hybridization, and EST data) and from multiple datasets. The Bgee team has manually curated thousands of samples, to annotate each of them to ontologies, correct for mistakes (often after direct contact with the authors of an experiment), and filter to keep only healthy, wild-type, high quality data. For instance, after careful curation, half of the samples from the GTEx experiment have been considered unsuitable to these healthy wild-type requirements, leading to discarding about 6000 of them from Bgee. Moreover, Bgee processes all curated samples to generate gene expression quantification, e.g., TPM values for each gene for bulk RNA-Seq, or CPM values for single-cell RNA-Seq data. Until recently, these annotations and processed expression values were available only through the Bioconductor R package BgeeDB (, with basic query capabilities. We have now built a user-friendly interface, on top of a JSON API, with advanced query capabilities. Thanks to this API and interface, it is possible to query data in Bgee using ontology reasoning, to retrieve, for instance, all samples annotated to “brain” including all its substructure (e.g., “white matter”, “hypothalamus”). Precise annotations are provided regarding: anatomical localization and cell type; developmental and life stage; strain; and sex. Each of these condition parameters can be precisely tuned for advanced querying of transcriptomics data. Additionally, it is possible to filter for presence of expression data for specific genes, experiments, or samples. The returned results include precise description of each sample, e.g. for single-cell RNA-Seq data, the sequencer used, sequenced transcript part, or forward/reverse strain studied. At Biocuration 2023 we will present our precise criteria of annotation, the capabilities of this new search tool and API, and examples of applications for enhancing discoveries in the transcriptomics field. These query tools are available since February 2023 at
  33. Curation and Terminology Strategies of the Immune Epitope Database
    Randi Vita, James A. Overton, Hector Guzman-Orozco, Bjoern Peters
    Expand the abstract
    The Immune Epitope Database (IEDB) relies upon manual biocuration, automated curation support tools, and a novel Source of Terminology (SOT) system to accurately and efficiently curate published experimental data from the literature. We use a team of PhD curators to achieve consistent data entry via manual biocuration with the assistance of sophisticated automated tools. The biocurators enter data into our web application which enforces a well-defined data structure. Our automated curation support tools enable automated query and classification of the literature, autocomplete functionality during the curation process, in form validation as soon as data is entered, ontology driven finder applications which retrieve appropriate ontology terms on the fly, and post curation data calculations. We exploit the logical relationships between ontology terms to drive logical validation. For example, our immune exposure process model enforces that only specific in vivo processes can lead to specific diseases, which in turn, can only be caused by specific pathogens. Our webform finder applications are ontology driven, use abbreviations and common names provided by the ontology experts, and allow curators to browse terms within the context of a hierarchical tree. Additionally, we are obligated to extend some external ontologies and resources to fully represent the immunology literature whenever we require the benefits of that pre-existing resource, but the needed child terms are beyond their scope. For example, SARS-CoV2 is a term found in NCBI, however, all the many variants of SARS-CoV2 which are very important to immunologists, and therefore, are critical for our curators to capture, are not. Our SOT system was recently developed out of the need to manage the use of many changing external ontologies. It incorporates diverse community data standards, provides for custom “immunology friendly” term labels, and manages custom term requests, as well as versioning. Interoperability is facilitated by a public API allowing users to resolve CURIEs for the terms utilized. Additionally, when we encounter terms that are not yet present in the public resources, we add temporary ontology terms, managed by our system, which are later replaced as new terms are created in the appropriate ontologies. We believe that our independent SOT system will be useful for other projects facing similar needs, regardless of which ontologies they are using.
  34. Mapping from virulence to interspecies interactions - an anotation review
    Antonia Lock, Sandra Orchard
    Expand the abstract
    Proteins in UniProt are automatically and manually annotated to keywords. Keywords may be mapped to various ontologies, resulting in automatic association of proteins with relevant ontology terms. If a mapped ontology term is obsoleted, a manual review process may be required in order to establish whether a suitable alternative term exists. The keyword KW-0843 Virulence was mapped to the now obsoleted Gene Ontology (GO) term GO:0009405 pathogenesis. This term was obsoleted from the GO as it was deemed out of scope of the ontology, and was superseded by terms pertaining to interspecies interactions. The loss of the mapping resulted in a loss of over 150,000 GO annotations. The ~150.000 proteins annotated to the keyword virulence (4144 manually annotated and 147,664 automatically annotated) were reviewed to assess if a new virulence keyword-GO term mapping could be created.
  35. Treatment Response Ontology in GENEVESTIGATOR
    Eva Macuchova, Jana Sponarova, Alena Jiraskova, Iveta Mrizova
    Expand the abstract
    In clinical trials, evaluating the treatment outcome is an essential feature of the success of therapeutics development. Analyses of patients' responder versus non-responder status can indicate clinically useful correlative biomarkers for therapeutic resistance. Objective response measurement is only applicable if it is performed on the basis of generally accepted, validated, and consistent criteria. Some patients' cohorts have detailed, multiple response measurements, while others have either no or sparse/ill-defined data. Response status is evaluated differently across the clinical trials, and it may be based on: a) disease-specific and well-established classification systems (e.g., response evaluation by RECIST in solid tumors, IMWG response criteria in multiple myeloma, EULAR response criteria in rheumatoid arthritis), b) time-point assessment (e.g., progression-free survival (PFS), overall survival (OS)), c) disease-specific and investigator-defined assessment criteria. To harmonize treatment response annotation in curated Genevestigator clinical datasets, we have built the Response Ontology that adheres to the terminology of the National Cancer Institute (NCI) Thesaurus, Common Terminology Criteria for Adverse Events (CTCAE), and other relevant resources. Development of the Response Ontology was preceded by a detailed analysis of the curated content in Genevestigator, including the identification of synonymous terms, followed by the unification of the curation rules. We will present the structure of the Response Ontology, the challenges of its development, and examples of its application in the curation of clinical data. The newly implemented Response Ontology allows Genevestigator users to identify and analyze treatment response status in cancer and autoimmune disease datasets. The architecture and nomenclature of the Nebion treatment Response Ontology have implemented FAIR Data principles. Moreover, the application of the Response Ontology facilitates not only machine-readable but also machine-actionable data.
  36. A general strategy for generating expert-guided, simplified views of ontologies
    Anita R. Caron, Josef Hardi, Ellen M. Quardokus, James P. Balhoff, Bradley Varner, Paola Roncaglia, Bruce W. Herr II, Shawn Zheng Kai Tan, Helen Parkinson, Mark A. Musen, Katy Börner, David Osumi-Sutherland
    Expand the abstract
    The use of common biomedical ontologies to annotate data within and across different communities improves data findability, integration and reusability. Ontologies do this not only by providing a standard set of terms for annotation, but via the use of ontology structure to group data in biologically meaningful ways. In order to meet the diverse requirements of users, and to conform to good engineering practices required for scalable development, biomedical ontologies inevitably become larger and more complex than the immediate requirements of individual communities and users. This complexity can often make ontologies daunting for non-experts, even with tooling that lowers the barriers to searching and browsing. We have developed a suite of tools that take advantage of Ubergraph ( to solve this problem for users that start from a simple list of terms mapped to a source ontology or for users who have already arranged terms in a draft hierarchy in order to drive browsing on their tools. This latter starting point is common among developers of anatomical and cell type atlases. A view generation tool renders simple, tailored views of ontologies limited to a specified subset of classes and relationship types. These views accurately reflect the semantics of the source ontology, preserving its usefulness for grouping data in biologically meaningful ways. A hierarchy validation system validates these user-generated hierarchies against source ontologies, replacing unlabelled edges with formal ontology relationships which can be safely used to group content. A review of hierarchical relationships that do not validate against source ontologies provides potential corrections to hierarchies and source ontologies. A combination of validation and view generation can be used to generate ontology views based on the provided hierarchy. Here we described the view generation and hierarchy generation tools and illustrated their use in generating views and validation reports for the HubMap Human Reference Atlas.
  37. Expanding the Repertoire of Causal Relations for a Richer Description of Activity Flow in Gene Ontology Causal Activity Models (GO-CAMs)
    Kimberly Van Auken, Pascale Gaudet, David Hill, Tremayne Mushayahama, James Balhoff, Seth Carbon, Chris Mungall, Paul Thomas
    Expand the abstract
    The Gene Ontology (GO) is the de facto bioinformatics resource for systematic gene function description. For over 20 years, gene function description using GO involved associating genes or gene products with terms from each aspect of GO: biological process (BP), molecular function (MF), and cellular component (CC). The power and utility of GO annotations have recently been extended with the introduction of GO Causal Activity Models (GO-CAMs)1. GO-CAM is a structured framework in which molecular activities from the MF aspect are connected in a causal chain using relations from the Relations Ontology (RO). The molecular activities are contextualized with terms from the BP and CC aspects, as well as external ontologies, with the ultimate goal of fully describing a biological system. Currently, GO-CAMs can be browsed on the GO site, downloaded from GitHub, and select causal diagrams viewed on gene pages at the Alliance of Genome Resources allowing biologists to see gene product relationships in the context of their activities. To enable GO curators to represent the richness of biological knowledge that can be represented in GO-CAMs, we: 1) expanded the repertoire of causal RO relations and 2) refined definitions of existing relations to ensure accurate and consistent modeling. We identified three main areas of classification: regulatory vs non-regulatory, direction of effect (i.e. positive or negative), and directness (i.e. temporal proximity of one activity to another). We refined the definition of regulatory relations in RO to include the restriction that they are specific, conditional effects and made a distinction between direct and indirect regulation, with the former used to describe regulation with no intervening activities, e.g. a regulatory subunit of an enzyme, and the latter used to describe cases where an upstream activity controls a downstream activity but with intervening activities between them, e.g. DNA-binding transcription factor activity and the activity of the gene product whose expression they control. In addition, we introduced more specific non-regulatory causal relations to describe constitutive effects in which an upstream activity is required, but normally present and not rate-limiting, for execution of a downstream activity. We also added a relation to link an upstream activity that removes small molecule inputs to a downstream activity when the former activity is not considered regulatory. A series of simple selection criteria has been implemented in the interface of the GO-CAM curation tool, Noctua, to guide curators in using the expanded repertoire of causal relations and ensure consistency and efficiency in curation. Lastly, for further reference, we created documentation on each causal relation, with usage guidelines and links to example models, on the GO Consortium’s wiki. The new causal RO relations and Noctua curation interface will enhance GO’s representation of complex biological systems using the GO-CAM framework. Thomas P.D. et al. (2019) Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nat. Genet., 51, 1429–1433.
  38. Data Curation and management of public proteomics datasets in the PRIDE database
    Deepti Jaiswal Kundu, Shengbo Wang, Suresh Hewapathirana, Selvakumar Kamatchinathan, Chakradhar Bandla, Yasset Perez-Riverol, Juan Antonio Vizcaíno
    Expand the abstract
    Introduction The PRoteomics IDEntifications (PRIDE) database at the European Bioinformatics Institute is currently the world-leading repository of mass spectrometry (MS)-based proteomics data [1]. PRIDE is also one of the founding members of the global ProteomeXchange (PX) consortium [2], and by January 2023, contain around 31,500 datasets (~83% of all ProteomeXchange datasets). PRIDE is an ELIXIR core data resource and ProteomeXchange has been recently named as a Global Core Biodata Resource by the Global Biodata Coalition [3]. Thanks to the success of PRIDE and ProteomeXchange (PX), the proteomics community is now widely embracing open data policies, a scenario opposite to the situation just a few years ago. Therefore, PRIDE has grown significantly in recent years (~500 datasets/month were submitted on average just in the past two years). Our major challenge is to ensure a fast and efficient data submission process, ensuring that the data representation is correct. Data handling and curation For each submitted dataset, a validation pipeline is first run to ensure that the data complies with the PRIDE metadata requirements and that the included files in the dataset are correctly formatted. Issues of different types can often occur. Therefore, the direct interaction between the PRIDE curation team and the users becomes critical. In the second step, the actual data submission takes place and dataset accession numbers are provided to the users. Finally, datasets are released when the corresponding paper is published. The stand-alone PX Submission tool is used by submitters in the data submission process. Some improvements have been made in the last year to improve the submission functionality and facilitate the handling of resubmissions and large datasets. It should also be noted that version 1.0 of the PRIDE data policy was released on May 2022 ( Conclusion The quantity, size, and complexity of the proteomics datasets submitted to PRIDE are rapidly increasing. The diversity of proteomics data makes a fully automated data deposition process very challenging, especially since data formats are complex and very heterogeneous. Curators then play a very active role in supporting the data submitters in the preparation and quality control of each PRIDE data submission. References [1] Perez-Riverol Y, Bai J, Bandla C, Hewapathirana S, García-Seisdedos D, Kamatchinathan S, Kundu D, Prakash A, Frericks-Zipper A, Eisenacher M, Walzer M, Wang S, Brazma A, Vizcaíno JA (2022). The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidence. Nucleic Acids Res 50(D1):D543-D552 (34723319) [2] Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, García-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA(2020). The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics, Nucleic Acids Res 48(D1):D1145-D1152 (31686107) [3] Global Biodata Coaltion 2022. Global Core Biodata Resources: Concept and Selection Process:
  39. ICGC ARGO Clinical Data Dictionary
    Hardeep Nahal-Bose, Peter Lichter, Ursula Weber, Melanie Courtot, Qian Xiang
    Expand the abstract
    The International Cancer Genome Consortium Accelerating Research in Genomic Oncology (ICGC ARGO) project is an international initiative to sequence germline and tumour genomes from 100,000 cancer patients, spanning 13 countries and 22 tumour types. ICGC ARGO will link genomic data to extensive clinical data from clinical trials and community cohorts concerning treatment and outcome data, lifestyle, environmental exposure and family history of disease for a broad spectrum of cancers. The goal will be to accelerate the translation of genomic information into the clinic to guide interventions, including diagnosis, treatment, early detection and prevention. A significant challenge is harmonizing large sets of clinical data from many different tumour types and programs globally. ICGC ARGO has developed a clinical data dictionary to collect high-quality clinical information according to standardized terminologies. The ICGC ARGO Clinical Data Dictionary was developed by the Ontario Institute of Cancer Research (OICR) Data Coordination Centre and the ICGC ARGO Tissue & Clinical Annotation Working Group. It defines a minimal set of clinical fields that must be submitted by all ARGO affiliate programs and is available at The ICGC ARGO Dictionary is an event-based data model which captures relationships between different clinical events and enables longitudinal clinical collection. It is based on common data elements (CDEs) and uses international standardized terminology wherever possible. The dictionary defines a data model consisting of 15 schemas in total. These include 6 core schemas: Sample Registration, Donor, Specimen, Primary Diagnosis, Treatment, and Follow up. There are also 5 schemas for submitting detailed treatment information regarding Chemotherapy, Immunotherapy, Surgery, Radiation, and Hormone Therapy. In addition, there are 4 optional schemas for collecting clinical variables encompassing exposure, family history of disease, biomarkers and comorbidity. The data model consists of 67 core fields and 104 optional extended fields. Each clinical field is defined by a data tier and an attribute classification, reflecting the importance of the field in terms of clinical data completeness, and validation rules are enforced to ensure data integrity and correctness. Furthermore, the data model is interoperable with other data models such as mCODE/FHIR (Minimal Common Oncology Data Elements), and is being used by several funded projects, including the European-Canadian Cancer Network (EuCanCan) and the Marathon of Hope Cancer Centres Network (MOHCCN). The ICGC ARGO data dictionary is a comprehensive clinical data model that ensures interoperability across other data models and standards and will enable high-quality clinical data collection that will be linked to genomic data to help answer key clinical questions in cancer research.
  40. MolMeDB - Molecules on Membranes Database
    Jakub Juračka, Kateřina Storchmannová, Dominik Martinát, Václav Bazgier, Jakub Galgonek, Karel Berka
    Expand the abstract
    Biological membranes are natural barriers of cells. The membranes play a key role in cell life and also in the pharmacokinetics of drug-like small molecules. There are several ways how a small molecule can get through the membranes. Passive diffusion, active or passive transport via membrane transporters are the most relevant ways how the small molecules can get through the membranes. There is an available huge amount of data about interactions among the small molecules and the membranes also about interaction among the small molecules and the transporters. MolMeDB ( is a comprehensive and interactive database. Data is available from 52 various methods for 40 biological or artificial membranes and for 184 transporters in MolMeDB. The data within the MolMeDB is collected from scientific papers, our in-house calculations (COSMOmic and PerMM) and obtained by data mining from several databases. Data in the MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method, transporter or dataset and we offer collected data openly for further reuse. Newly are data available in RDF format and can be queried using SPARQL endpoint ( Federated queries using endpoints of other databases are also possible. Lately this database has been used for analysis of different functional groups influence on molecule-membrane interactions.
  41. - a new curated resource for cancer cell line variants
    Rahel Paloots, Ellery Smith, Dimitris Giagkos, Kurt Stockinger, Michael Baudis
    Expand the abstract
    Cancer cell lines are important models for studying the disease mechanisms and developing novel therapeutics. However, they are not always good representations of the disease as they accumulate mutations over propagation. Additionally, due to human error, cancer cell lines can become misidentified or contaminated. Here, we have collected a set of cancer cell line variant data to facilitate identification of suitable cell lines in research. Our dataset includes both structural and single nucleotide cancer cell line variants. The set of copy number variants (CNVs) originates mainly from Progenetix - a resource for cancer copy number variants. All available cell lines from Progenetix have been mapped to Cellosaurus (a cell line knowledge resource). In total, over 5000 cancer cell line CNV profiles are available for over 2000 distinct cell lines. Additionally, we have a curated set of annotated cancer cell line single nucleotide variants (SNVs) from ClinVar and a collection of known SNVs from CCLE. Curated variants include information about pathogenicity of the variant as well as clinical phenotype associated with this variant. Moreover, to get additional CNVs and associated metadata we performed data mining by using natural language processing tools. The results are displayed on an interactive CNV profile graph- a novel feature that allows for the selection of region of interest and shows publications associated with the area. Here, we introduce the features and data included in the database that are publicly available and freely accessible.
  42. The BioGRID Interaction Database: SARS-CoV-2 Coronavirus Interaction Networks and Genome-wide CRISPR Phenotypic Screens
    Rose Oughtred, Bobby-Joe Breitkreutz, Chris Stark, Lorrie Boucher, Christie Chang, Sonam Dolma, Genie Leung, Nadine Kolas, Jennifer Hunt, Frederick Zhang, Jasmin Coulombe-Huntington, Andrew Chatr-Aryamontri, Kara Dolinski, Mike Tyers
    Expand the abstract
    The Biological General Repository for Interaction Datasets (BioGRID: is an open-access database that archives and freely disseminates genetic, protein and chemical interaction data curated from the primary literature on human and major model organisms. As of December 2022, BioGRID contains over 2,579,000 structured records for biological interactions captured from high and low throughput studies documented in more than 70,000 publications, as curated from 179,325 papers read in total. Curation is carried out at a species level and a themed project level. Comprehensive literature curation coverage for the budding yeast Saccharomyces cerevisiae has produced over 795,000 interactions, which are shared with the Saccharomyces Genome Database (SGD), a member of the Alliance of Genome Resources ( BioGRID also collaborates with other Alliance members, including WormBase, FlyBase, and PomBase, to help increase the impact of data collection and minimize curation redundancy. Project level curation is used to provide in-depth coverage of specific areas of biomedical or biological interest. A recent project is focused on genetic, protein and chemical interaction data for COVID-19 research. BioGRID has systematically curated >34,000 protein interactions for SARS-CoV-2, as well as the related SARS-CoV and MERS-CoV coronaviruses, from more than 760 preprints and published articles. The COVID-19 project has also captured over 100 coronavirus-related chemical-protein relationships directly curated from the literature in addition to incorporating chemical-protein interactions for human drug targets drawn from DrugBank and BindingDB. Other themed BioGRID projects include specific biological processes with disease relevance such as autophagy and the ubiquitin-proteasome system (UPS), as well as diseases such as Fanconi anemia and glioblastoma. BioGRID also curates genome-wide CRISPR-based genetic screens in human and model organism cell lines. These high throughput gene-phenotype datasets are housed in the Open Repository for CRISPR Screens (ORCS) ( ). This extension of BioGRID currently contains 1,678 genome-wide single mutant phenotype screens. CRISPR technology has also recently been applied to various virus infection models in human or African green monkey cell lines to identify host genes that confer susceptibility or resistance to infection. To date, we have curated 87 CRISPR phenotype screens for SARS-CoV-2, SARS-CoV, MERS-CoV, and other related coronaviruses. These screens have identified both known pro-viral and anti-viral factors, as well as potential new therapeutic drug targets. All data in BioGRID and ORCS are publicly available and may be freely downloaded in standardized formats that can be readily incorporated into various applications for computational analyses. BioGRID and ORCS data are also provided through model organism databases (MODs) and meta-databases including UniProt, NCBI and PubChem. This project is supported by the National Institutes of Health Office of Research Infrastructure Programs [R01OD010929 to M.T., K.D.].
  43. Curation of Rare Disease Data in the Rare Disease Cures Accelerator-Data and Analytics Platform
    Nicole Vasilevsky, Ian Braun, Diane Corey, Emily Hartley, Daniel Olson, Will Roddy, Ramona Walls
    Expand the abstract
    It is estimated that there are 10,000 unique rare diseases, which are defined as diseases that affect one in every 2,000 patients worldwide. Given the infrequency of these diseases, patients and their families often experience challenges with obtaining a diagnosis and/or finding effective treatments or a cure for the disorders. While clinical data is available describing the phenotypes, genotypes, and other clinical features of many diseases, this data is often disparate and siloed and not collected or described in a standardized manner. The Critical Path Institute (C-Path) is a non-profit, public-private partnership with the US Food and Drug Administration that fosters the development of new evaluation tools to inform medical product development. C-Path is developing the Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) as an accessible data portal for researchers, clinicians, and patients. RDCA-DAP contains aggregated, curated data to support and accelerate rare disease characterization and the development of therapies. Patient-level data is shared with the RDCA-DAP from various organizations and companies and primarily consists of clinical trials, observational studies, and rare disease patient registry data. Data can be freely searched and made available to users via the ‘FAIR Data Services' platform upon completion of a data access request and data use agreement. ‘Workspaces’ provide a cloud-based infrastructure where users can view the data and perform analytical workflows to generate new insights. Data in RDCA-DAP undergoes a responsive and rigorous curation workflow to ensure data integrity, security, and privacy and to conform to the FAIR principles of being findable, accessible, interoperable, and reusable. The workflow is broken down into pre-standardization and standardization levels. At the pre-standardization level, manual and automated checks are performed to ensure compliance with privacy and security measures, data cleaning is done to ensure consistency, and data dictionaries and catalogs are created and published on the platform. At this point, metadata describing the dataset is made available in FAIR Data Services, and users can request access. In the standardization step, selected data such as demographic information, laboratory tests, and clinical features are mapped to the OMOP common data model (CDM). The process is responsive in that curation priorities are based on user requests, and if requested by researchers, additional variables will be mapped to OMOP. Further curation is done to map data to OBO Foundry ontologies using the Critical Path Ontology (CPONT). CPONT is an application ontology that imports various OBO ontologies for the features, including data about diseases, phenotypes, or assays. Standardization to these OWL ontologies provides an additional layer of semantics for integrating datasets across disease areas and enables exploration and discovery through the C-Path Knowledge Graph. Providing semantically standardized rare disease data to the research and clinical community will facilitate novel queries across with the aim of developing new tools to accelerate the development of new treatments.
  44. A Gene Summary Generation System for Animal Genomes
    Valerio Arnaboldi, Ranjana Kishore, Paul Sternberg
    Expand the abstract
    Knowledgebases that curate and organize gene data also provide manual text paragraphs that describe gene function. These short paragraphs, referred to as gene summaries, are valued by users for the ease with which they convey key aspects of gene function. However, the manual writing of gene summaries is time- and labor-intensive and does not scale to match the fast-growing scientific literature. Our solution to these challenges is a system that automatically generates reader-friendly concise gene summaries that simulate natural language and are based on curated, structured gene data referred to as annotations. This system is currently implemented at WormBase and the Alliance of Genome Resources and leverages the different types of professional, specialized gene-related data curation such as Gene Ontology (GO) curation, orthology predictions, gene expression curation, etc. and does not require the user to be familiar with the database, data structures, or biological and biomedical vocabularies. One of the main features of our method is the use orthology data to generate summaries for: (i) less studied genes, thus providing more coverage of genomes; (ii) genes that are relevant to the study of human disease pathogenesis, facilitating the discovery of established and potential models of disease; (iii) genes in species that can be related to a “reference species”, thus providing summaries for genes where no formal curation projects exist. In addition, the modularity and flexibility of the system allows the gene summaries to be updated frequently. Recently, we improved gene summaries at the Alliance by including gene product-to-term relationships from the GO to more accurately reflect gene function, for example, to describe whether a gene is directly “involved in” a biological process or “contributes to” a molecular activity. Further, we have added gene summaries for two frog species, X. laevis and X. tropicalis demonstrating that the system is readily generalizable. We plan to expand the system to many more species and more data types, and also include AI language models (such as BERT and GPT3) to generate summaries on unstructured data from the literature. The methods, workflow and examples will be described in the poster.
  45. The Role of GigaDB in the Curation of Vector-Borne Disease Data
    Chris Armit, Mary Ann Tuli, Yannan Fan, Nafisa Qazi, Christopher Hunter, Scott Edmunds, Laurie Goodman
    Expand the abstract
    A unique opportunity in 2022 was the partnership between GigaScience Press, the Global Biodiversity Information Facility (GBIF), and the TDR, the Special Programme for Research and Training in Tropical Diseases hosted at the World Health Organization (WHO). The focus of this partnership was the publication of new datasets that present biodiversity data for research on vectors of human diseases. Data Release papers from this partnership on vectors of human disease were published in GigaByte Journal as part of a thematic series, which mobilised more than 500,000 occurrence records and 675,000 specimens from more than 50 countries. Due to the international nature of the vector-borne disease data, the thematic series was published in multiple languages. The role of the GigaDB team in this partnership was to assist the data peer review process, and to provide the necessary support for Data Release papers that required curation. This involved data auditing and providing a data review for each submission, and ensuring the data were open and FAIR (Findable, Accessible, Interoperable and Reusable). The data review included selecting a number of data points to be carefully inspected, and verifying that the total number of occurrences and the geographic range were consistent with the details in the paper. In line with Open Peer Review, the peer reviews and data review templates are made publicly available via the Article Review History tabs on all of the papers ( Following the success of the first series of papers, TDR, GBIF and GigaScience Press have announced a second call for authors to submit Data Release papers on vectors of human disease.
  46. Should data fields in biological resources be labeled more FAIRly?
    Susan Tweedie, Bryony Braschi, Liora Vilmovsky, Tamsin Jones, Ruth Seal, Elspeth Bruford
    Expand the abstract
    The HUGO Gene Nomenclature Committee (HGNC,, now acknowledged as a Global Core Biodata Resource (GCBR), is responsible for approving unique symbols and names for human loci, including protein coding genes, ncRNA genes and pseudogenes, to allow unambiguous scientific communication. We also name genes in selected vertebrates via our sister project, the Vertebrate Gene Nomenclature Committee (VGNC, Approved nomenclature from our websites, together with curated alias symbols and names, are widely displayed by other resources including Ensembl, NCBI Gene, The Alliance of Genome Resources and UniProt, which aids FAIR data sharing. However, our data (and other data) are often labeled differently when they appear on another resource’s website; what we call an “Approved symbol” may be labeled as a “Gene name”, which may help to explain why many of our users confuse the terms “name” and “symbol”. There is the additional complication that different resources display different levels of granularity e.g. grouping aliases for gene names and gene symbols together versus keeping them separate. For other data types, many different terms are used to refer to exactly the same data – is this causing confusion for users? At a time when FAIR principles are encouraged and the use of ontologies is widespread, is it worth devoting more effort to standardizing labels for data fields? We first raised this issue in a poster at the 1st UK Local Biocuration Conference in May 2022 and we would now welcome input from the wider biocuration community. Our updated poster expands our comparison of field labels across key biological resources to include other GCBRs that display gene data, and we suggest consensus terms that could be used for a more standardized approach, which could help users to navigate between resources. Focusing on nomenclature terms, we also consider how well existing ontologies align with the terms used in databases and discuss whether new terms or a dedicated ontology would be helpful.
  47. PhenoMiner: improved interfaces enhance usability of RGD's quantitative phenotype data repository
    Mary Kaldunski, Jennifer R. Smith, Stan Laulederkind, G. Thomas Hayman, Shur-Jen Wang, Monika Tutaj, Mahima Vedi, Wendy Demos, Adam Gibson, Logan Lamers, Ketaki Thorat, Jyothi Thota, Marek Tutaj, Jeff De Pons, Melinda Dwinell, Anne Kwitek
    Expand the abstract
    The Rat Genome Database (RGD, is the principal resource for data related to rat biomedical research for genome, phenotype, and disease. The data collection is the result of both manual curation by RGD curators, and data importation from other databases through custom pipelines. RGD has developed a growing suite of innovative tools for querying, analyzing, and visualizing this data, making it a valuable resource for researchers worldwide. One recently updated platform is the PhenoMiner data repository with its concomitant data mining tool components. PhenoMiner was developed for rat quantitative phenotype measurement data from both manual curation of scientific literature and direct data submissions by investigators. PhenoMiner enables users to query and visualize quantitative phenotype data across rat strains and multiple studies. The data includes detailed information about what (Clinical Measurement Ontology - CMO), how (Measurement Method Ontology - MMO), and under what conditions (Experimental Conditions Ontology - XCO) phenotypes were measured, and in what animals (Rat Strain Ontology - RS) for each measurement value. A recent project included curation for strains in the Hybrid Rat Diversity Panel, especially those previously underrepresented in the data repository. To increase curation efficiency, the data input interfaces have been improved, including enhanced ability to view, clone, and edit multiple records at a time, which decreases the amount of time required for entering study data. Quality control checkpoints in place for individual data entry have been expanded to encompass bulk data loading. In response to user feedback, the public-facing data mining tool was reworked to improve the user interface (UI) for data interactivity. Improvements in the search functionality allow straightforward navigation of complex datasets, and the new query results page simplifies filtering to facilitate tailoring of specific query results. Users are no longer required to return to the front page or to start a new query to remove some conditions or other components. New functionality makes it possible to view results for related terms with the same unit of measurement in the same graph. Data can be downloaded either as filtered results or all results matching the user's original query, providing users with data at multiple levels. Upcoming planned improvements to the user interface will include the ability to visualize as well as download imported high throughput phenotyping data from individual inbred and outbred (e.g., heterogeneous stock) rats. RGD is advancing the utility of PhenoMiner by streamlining curation with increased ability to input data accurately and efficiently, and via improvements in the public UI enabling better data access, filtering, and visualization, with continued ability to download data for further analyses.
  48. Biological Curation for ChEMBL: Towards the consistent assignment of targets for extracted bioactivity data
    Sybilla Corbett, Emma Manners, Melissa Adasme, Ricardo Arcila, James Blackshaw, Nicolas Bosc, Eloy Felix, Fiona Hunter, Harris Ioannidis, Tevfik Kiziloren, David Mendez Lopez, Maria Paula Magarinos, Juan F. Mosquera, Marleen de Veij, Barbara Zdrazil, Andrew R. Leach
    Expand the abstract
    ChEMBL is a manually curated database of bioactive molecules with drug-like properties, recently named by the Global Biodata Coalition as a Global Core Biodata Resource. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs. Data in ChEMBL is taken from a range of sources, including publications, patents and deposited data from collaborators. All data is curated and standardised, both with manual and automatic checks; there are 2.3 million compounds, 1.5 million assays and 15 thousand targets in the most recent ChEMBL release. Compounds are entered from literature, approved drugs and clinical research; structures are standardised, stripped of their salts, linked by hierarchy and their properties calculated. Assays are assigned targets, varying in scale from whole organisms to single proteins, and additional experimental entities are assigned using existing ontologies. A description is provided for each assay, as well as an assay type (for example ADME, Functional, Binding). Activity values and units are standardised, with steps for flagging outliers and detecting duplicates. A pCHEMBL value is calculated, providing roughly comparable measures across different activity types. Manual checking of these steps plays an integral role in ensuring data quality, alongside automated curation. Loading data to ChEMBL is largely by invitation; we also accept submissions of high quality bioactivity data. As well as the relationships between compounds, bioactivity data and assay conditions, ChEMBL is also able to store assay parameters and supplementary activity data. While the curation effort to incorporate this data is higher, it provides greater complexity and greater value to the database. ChEMBL is guided by the FAIR principles. The layers of curation and the use of external ontologies contribute towards the findability, interoperability and reusability of data; ChEMBL IDs are stable identifiers. For the purposes of accessibility, ChEMBL has a powerful web interface (, and data is provided under CC BY-SA 3.0 license. The database can also be downloaded from the FTP site, or accessed by API or RDF/SPARQL. This poster highlights the provenance of ChEMBL data and the curation processes used to prepare it for addition to the database. Target and assay types are listed, and a broad outline of the steps for compounds, bioactivity data, and experimental variables are shown. A more detailed process for tissue mapping is given as an example of our workflows. We list the different methods for accessing ChEMBL data and resources for help and information about the database.
  49. The importance of good data quality and proper pathogenicity reporting in the medical genetics field: the case of oligogenic diseases
    Sofia Papadimitriou, Barbara Gravel, Charlotte Nachtegael, Elfride De Baere, Bart Loeys, Mikka Vikkula, Guillaume Smits, Tom Lenaerts
    Expand the abstract
    Although standards and guidelines for the interpretation of variants identified in genes that cause Mendelian disorders have been developed, this is not the case for more complex genetic models including variant combinations in multiple genes. 318 research articles reporting oligogenic cases were extracted from PubMed. A transparent curation protocol was developed assigning a confidence score to each oligogenic case based on the amount of pathogenic evidence at the genetic and functional level, based on the relevant oligogenic information collected by independent curators (i) from the articles and (ii) from public relevant databases. The collection and assessment of this data led to the creation of OLIDA, the Oligogenic Diseases Database. OLIDA contains information on 1229 oligogenic cases linked to 177 different genetic diseases. Each instance is linked with a confidence score depicting the quality of the associated genetic and functional pathogenic evidence. The curation process revealed that the majority of papers do not provide proper genetic evidence refuting a monogenic model, and rarely perform functional experiments for confirmation. Our recommendations stress the necessity of fulfilling both conditions. The use of multiple extended pedigrees showing a clear segregation of the reported variants, control cohorts of a suitable size, as well as functional experiments showing the synergistic effect of the involved variants are essential for this purpose. With our work we reveal the recurrent issues on the reporting of oligogenic cases and stress the need for the development of standards in the field. As the number of papers identifying oligogenic causes to disease is increasing rapidly, initiating this discussion is imperative.
  50. To realize more interconnected RDF data
    Yasunori Yamamoto, Takatomo Fujisawa
    Expand the abstract
    RDF data show their values the most when built in a distributed manner and linked to each other from several aspects with URIs as the keys. In the life science domain, lots of RDF datasets have been built, and they are the largest numbers in the latest linked open data cloud. However, we have seen multiple URI assignment for an identical resource and several URI mismatches that should be identical from case discrepancies to misuse of symbols such as '#' and '_'. As a result, the knowledge graph is not effectively used because it prevents linkings between RDF data such as genomes, proteins, glycans, and compounds. Therefore, RDF curation is needed to make RDF data more linkable and valuable. Here, we propose a tool called RDF-doctor for RDF data builders to curate them. RDF-doctor is a command line interface (CLI) tool, and we assume the following use scenario. First, an RDF data builder generates a draft version by using some RDFization tools. Second, RDF-doctor accepts this draft dataset and generates the following three datasets from it: Shape Expressions (ShEx) schema, a prefix list, and a list of classes and properties. ShEx schema is generated by a tool called sheXer. These prefixes, the classes, and the properties are respectively clustered to find any string fluctuations mentioned above including spelling variants and errors. Third, the data builder edits the ShEx schema or modifies the RDFization tool based on the information shown by RDF-doctor. Since the generated ShEx schema reflects the given RDF data, we expect the data builder can easily understand it even though he/she is not familiar with ShEx. In addition, a ShEx schema can be used to validate RDF data, and therefore the data builder can use it to validate the data generated by the tool. ShEx schema generated by sheXer contains statistical information such as the ratio of the subject URI count that matches a specific shape expression to the total number of those URIs belonging to a specific class. Therefore, the data builder can notice an error if any when there is an outlier. We assume that the above curation cycle would be iterated a couple of times to refine the resultant RDF dataset. While RDF-doctor as a CLI tool is under development, essential features mentioned above are provided as separated tools such as sheXer to generate ShEx schema and OpenRefine to make clusters. To extract prefixes from a RDF dataset, we have implemented a python script. In this situation, we have conducted a feasibility study by applying our proposed curation method to an RDFization project and obtained a promising result. As a next step, we will provide the CLI tool and get feedback from prospective users. Concerning the multiple URI assignment issue, we will add a function to RDF-doctor, which suggests a widely used vocabulary based on a given dataset.
  51. Updating the Curation Status of tRNA Modification Pathways in the Database of Clustered Orthologous Genes
    Colbie Reed, Michael Galperin, Eugene Koonin, Valerie de Crecy-Lagard
    Expand the abstract
    The ‘deluge of data’ brought about by the contemporary post-genomic era has led to a growing demand for high quality curation and stewardship of biological data. The continued advancement of bioinformatic techniques and data analysis have stoked pressure across the bioinformatics community to improve the precision and representativeness of functional subgroups, and this includes more novel subsets of cellular biology like nucleobase modification. The Database of Clustered Orthologous Genes (COG Database) within the National Center for Biotechnology Information (NCBI, NLM/NIH) stands as one of the first databases dedicated to defining putative function through orthology-guided functional annotation and furthering homology-driven research by making these defined groups referenceable for the greater scientific community. The modification of tRNA has become of significant interest for better understanding many complex biological phenomena, this being particularly true within the context of human health as it concerns the NIH (e.g., chronic illness, quality of life, aging, etc.). Here, specific COGs linked to tRNA modification pathways are examined within and without the classification schemes of the COG Database, investing specifically in the improved quality and completeness of this subset of pathways within the database. Both manual and computationally facilitated biocuration approaches were implemented, revealing surprising lapses among COG annotations and that of some genomes (negating the fact that some of the benchmark genomes still used by the COG database have been decommission by their parent entity, NCBI). Recognition of the overlaps and differences in tRNA, DNA, m/rRNA modification pathways between taxa was necessary to properly assemble the collection of tRNA-specific modification genes as presented in the COG database. Here, we focus on a particular pathway to exemplify this work and its potential utility in fundamental and translational research (i.e., members of the MnmACDEGH pathway). These results intend to guide the improvement of COG functional annotations, as well as the overall reliability of future COG reference and propagation.
  52. Protein Data Bank in Europe Knowledge Base (PDBe-KB) - Creating knowledge from macromolecular structures and functional annotations
    Deborah Harrus, Joseph Ellaway, Pdbe-Kb Consortium
    Expand the abstract
    The Protein Data Bank (PDB) contains over 200,000 macromolecular structures, referencing more than 61,000 unique UniProtKB entries. However, extracting a full biological context for these molecules can be challenging, given the scattered nature of relevant information across hundreds of specialised resources. Biological data resource curators would benefit from a comprehensive resource that aggregates all appropriate structural and functional annotations to enhance the inherent value of macromolecular structures. PDBe-KB ( was launched in 2018 as a collaborative effort between PDBe and a diverse group of biological resources and structural bioinformatics research teams in response to this need. PDBe-KB aggregates a wide range of structural and functional annotations, including ligand binding and catalytic sites, protein-protein interfaces, post-translational modification, and physicochemical parameters. PDBe-KB serves as a dynamic, community-driven resource for users seeking comprehensive annotations of macromolecular structures. By providing a single portal for accessing a wide range of annotations, PDBe-KB enhances the value of these structures and saves significant time and effort in collecting and comparing information across multiple sources, regardless of whether it was manually curated or computationally predicted. Managed by the PDBe team at EMBL-EBI, PDBe-KB brings together 34 data resources from 13 countries to offer a comprehensive and unified view of the biological context of macromolecular structures. It is an essential tool for biological data resource curators who seek to answer complex biological questions and enhance the inherent value of macromolecular structures.
  53. ChEBI: where chemical entities and ontology meet
    Adnan Malik, Carlos Moreno, Muhammad Arsalan, Juan Felipe Mosquera Morales, Eloy Félix, Andrew Leach
    Expand the abstract
    ChEBI ( was first introduced in 2004 and has since grown to more than 60,000 manually curated entries, comprising natural products, synthetic compounds, subatomic particles and chemical classes. New chemical entities are continuously deposited into ChEBI by its growing user community. Each distinct chemical structure in ChEBI is assigned a stable and unique identifier (ChEBI ID), which is used by multiple resources for compound identification and information. The database serves as a human-expert-curated source of chemical structures, nomenclature, metabolite species information and database cross-references. ChEBI provides cross-reference links to several different domain-specific databases including Gene Ontology, PDBe, UniProt, MetaboLights, Reactome, Rhea, BioModels, and Europe PMC, amongst many others. ChEBI's ontology defines a hierarchical classification for chemical entities, where relationships between chemical entities or classes of chemical entities are specified. The ChEBI ontology consists of a molecular structure sub-ontology where chemical entities are classified according to composition and structure (E.g., tertiary amine, carboxylic acid etc) and a role sub-ontology which classifies chemical entities on the basis of their role within a chemical context (E.g., solvent), biological context (E.g., inhibitor), or application (its intended use by a human E.g., anti-inflammatory agent, agrochemical etc). ChEBI's ontology is widely used for knowledge-based automated reasoning and is semantically integrated with many other biological ontologies. All of the information and data in ChEBI is freely available and downloadable in several file formats. In this poster we will describe the current ChEBI database and the detailed curation process that are followed to add new entries.
  54. The GlyCosmos Portal: integrated glycan-related omics data and inferencing across organisms
    Kiyoko F Aoki-Kinoshita, Issaku Yamada, Yasunori Yamamoto
    Expand the abstract
    The GlyCosmos Portal [1] has been developed using Semantic Web technologies, so we attempted to perform inference on its Semantic Web data. As a simple proof-of-concept, we focused on the organisms that have been accumulated across glycans, glycoproteins, pathways, diseases, etc. in GlyCosmos. As of version 3.0., there were species whose distinct strains were annotated to contain different glycans, but taxonomies that were at higher levels to that species (such as genus or kingdom) were not annotated with the same data, despite the fact that logically, they should contain all the annotations that have been applied to the species within its hierarchy. For example, the species Aspergillus oryzae had 16 glycans, 5 glycoproteins, and 1 lectin entry. However, the genus Aspergillus was annotated with only 10 glycans, whereas it should also contain the glycans, glycoproteins, etc. for all of the species under the Aspergillus genus. To address this issue, we used the semantics in the GlyCosmos triplestore to perform inferences on these taxonomic annotations. First, we obtained the whole taxonomic hierarchy from NCBI and stored it in our datastore as triples. Then, we formulated inference rules to automatically obtain the higher taxonomies for any particular taxon. As a result, we were easily able to formulate queries that would automatically annotate the information from the lower taxons to the higher taxons, thus returning information that was semantically accurate. Note that we did not have to generate any new data to specifically indicate that the data associated with a lower taxon was also associated with the higher. Based on this work, we have fully integrated the inferenced organism data into GlyCosmos in the latest update in April, 2023. Thus, the discrepancies regarding glycans and the organisms in which they are found have been resolved. We plan on attempting other inferences on glycan structures next in order to further enrich the data in GlyCosmos. [1] I. Yamada et al., “The GlyCosmos Portal: a unified and comprehensive web resource for the glycosciences,” Nat. Methods, vol. 17, no. July, pp. 649–650, 2020, doi: 10.1038/s41592-020-0879-8.
  55. Curating Phenotype and Disease Annotations in ZFIN to Increase Model Organism Data Relevant to Structural Birth Defects and Cancer
    Sridhar Ramachandran, Leyla Ruzicka, Yvonne Bradford, Monte Westerfield, Cynthia Smith
    Expand the abstract
    In recent years, translational research has gained importance as a way to use the aggregated phenotype and gene function data generated from model organism research to gain a better understanding of human disease etiology. Phenotype annotations present a challenge for translation research due to each organism using a species-specific phenotype ontology, with the use of the Human Phenotype Ontology (HPO), Mammalian Phenotype Ontology (MPO) and the Phenotype and Trait Ontology (PATO) and Zebrafish Anatomy Ontology (ZFA) to represent human, mouse and zebrafish phenotypes respectively. To effectively translate the phenotype data between species, the phenotype terms need to have semantic similarities using similar relationships or definitions. ZFIN and MGI phenotype editors participate in cross-species phenotype reconciliation efforts organized through the Phenotype Ontologies Traversing All The Organisms (POTATO) workshops. The Zebrafish Information Network (ZFIN, and Mouse Genome Informatics (MGI, are working together on focused phenotype curation of diseases from Kids First ( data resource to increase curated model organism data for structural birth defects and pediatric cancer. The limited amount of pediatric data available increases the importance of using model organism data to bridge knowledge gaps in our understanding of these diseases. A broad overview of the curation approach is described in the poster presented by Susan M. Bello et al. (‘Using Disease Focused Curation to Enhance Cross Species Translation of Phenotype Data.’) Curation of the Idiopathic Scoliosis and Cleft Palate as initial diseases of interest identified several gaps in curation and inconsistencies in the use of phenotype annotations to describe the zebrafish disease models. The concerted curation effort led to the closing of curation gaps in ZFIN for these diseases. However, variability in phenotype annotations present a problem with the standardization of data needed to conduct robust translational research. Curated phenotype statements from publications were reviewed and analyzed to determine if these annotations could be mapped to terms in the MPO and HPO that have been used to annotate idiopathic scoliosis and cleft palate. Standardized phenotype statements relevant to idiopathic scoliosis and cleft palate like “vertebral column - rotational curvature, abnormal” and “palate – morphology, abnormal” were identified by the ZFIN curation team as statements important for identifying zebrafish models of these diseases. These statements were added to existing phenotype annotations in ZFIN. These expanded annotations are available on ZFIN web pages and download files. We are also collaborating with MGI to make these available in the Kids First data portal to allow researchers easier access to cross-species information on genes and disease models.
  56. FlyCyc: updating the metabolic network for Drosophila melanogaster
    Steven Marygold, Phani Garapati, Gil dos Santos, Peter Karp
    Expand the abstract
    BioCyc is a collection of Pathway/Genome Databases (PGDBs) that represent metabolic networks for over 20,000 species. The BioCyc ‘Pathway Tools’ software can generate a metabolic reconstruction for a given species, stored as a PGDB, by matching enzymes in the functionally annotated genome of that species with the reactions/pathways in the reference metabolic database (MetaCyc). The quality of the PGDB therefore depends on that of the underlying functional annotations. A PGDB for Drosophila melanogaster (FlyCyc) exists but is based on data from a FlyBase release 15 years ago and therefore excludes any changes to genomic or functional annotations made since then. We recently conducted a systematic review of Drosophila enzymes, improving the coverage and accuracy of their functional (Gene Ontology molecular function (GO-MF) and Enzyme Commission (EC)) annotations and creating accessible ‘gene group’ pages for each enzyme class in FlyBase. We verified ~3,750 Drosophila enzymes, made ~4,000 changes to manual GO annotations, and organized the enzymes into ~800 hierarchical groups. In so doing, we identified ~300 issues regarding catalytic activity terms within the GO (e.g. incorrect term relationships, missing EC cross-references) and ~400 issues with automated annotation pipelines (InterPro2GO, PAINT, UniRule). Almost all these issues have now been addressed, thereby improving the quality of enzymatic GO annotation for all species. We have used data from the latest FlyBase release (FB2023_02) and the Pathway Tools software to recompute an updated FlyCyc that incorporates our improved GO/EC annotations as well as the latest genomic and gene nomenclature data. Compared to the previous version, the updated FlyCyc includes 45 additional metabolic pathways and identifies >600 additional enzyme-encoding genes. Ambiguous enzyme mappings and ‘pathway holes’ are being resolved as far as possible by correcting GO-MF/EC annotations within FlyBase to focus primary curation activities within a single database. The finalized collection of Drosophila metabolic pathways will then be made available on the BioCyc website. In addition to providing researchers with improved metabolic pathway diagrams, this update will enhance the functionality of various ‘omics data analysis tools available at BioCyc. Going forwards, we will review GO cellular component annotations to Drosophila enzymes to accurately indicate the subcellular compartment in which they act and, where applicable, the macromolecular complex of which they are a part. We will also perform a systematic annotation review of Drosophila transporters, as they also play a critical role in metabolic pathways. These additional enhancements, together with ongoing improvements to enzymatic annotations, will be reflected at FlyCyc by establishing regular 6-monthly synchronizations with the data at FlyBase.
  57. Equity, Diversity, and Inclusion in the International Society for Biocuration
    Mary Ann Tuli, Yvonne Bradford, Alice Crowley, Pratibha Gour, Rachael Huntley, Tiago Lubiana, Shirin Saverimuttu, Nicole Vasilevsky, Roxanne Yamashita, Monica Munoz-Torres
    Expand the abstract
    The Equity, Diversity, and Inclusion (EDI) committee was formed in 2019 as an outcome of a workshop held at the 12th International Biocuration conference in Cambridge, UK. This first workshop sparked some lively discussions as well as a great deal of interest within the community. It was widely agreed that the workshop format was preferred over session presentations as it allowed for more debate and for attendees to discuss personal experiences. The current committee is made up of 10 members and in contrast to the other International Society for Biocuration (ISB) committees it includes members who are not also on the Executive Committee (EC); 2 of the existing members also sit on the EC and one member has professional EDI training. We meet monthly and discuss ongoing tasks, plan future goals, respond to any issues that have come to our attention, discuss, and implement our objectives, and prepare a report for the monthly EC meetings. As well as making available past presentations, relevant publications, and documents, we have written a Code of Ethics and Professional Conduct, which can be seen on the ISB website ( This document is regularly revised with the help of our EDI expert. We encourage members of the ISB to get in touch with us regarding any EDI issue they may have, or an issue they feel we have not addressed. Anyone with an interest in EDI issues is welcome to join the committee, though we would particularly welcome people from Asia, Africa and Eastern Europe as these regions are currently under-represented. If you are interested in joining the committee, please contact
  58. PhEval - Phenotypic Inference Evaluation Framework for variant and gene prioritisation algorithms
    Vinicius de Souza, Yasemin Bridges, David Osumi-Sutherland, Julius Jacobsen, Nicolas Matentzoglu, Damian Smedley
    Expand the abstract
    Diagnosing rare diseases is a difficult problem which requires the integration of complex data types such as ontologies, gene-to-phenotype associations and cross-species data and their exploitation by variant and gene prioritisation algorithms (VGPA). VGPAs that leverage these types of data have been implemented by sophisticated tools such as Exomiser and Phen2Gene. However, leveraging this kind of data requires complex processes such as ontology matching, phenotype matching and cross-species inference. Many factors can impact the performance of these variant prioritisation algorithms, for example, ontology structure or annotation completeness. The lack of an empirical framework to assess its efficacy currently hinders the ability of effective phenotype matching and improvement of prioritisation tools. For this reason, we created PhEval, an extensible and modular Phenotypic Inference Evaluation Framework. PhEval can process different inputs, such as phenopackets, a schema for sharing disease and phenotype information that characterizes a person or biosample, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. During its data preparation phase, PhEval creates standard input data to be processed and can also calculate Semantic Similarity Profiles that will be used for algorithms that attempt to establish connections among biological entities of interest by using a variant prioritization strategy in the runner phase. PhEval offers extensibility to developers, allowing them to create specific/concrete implementations that integrate various variant prioritization algorithms invoked through the PhEval Runner interface. Lastly, in the analysis phase, PhEval produces extensive statistical reports output based on standardised results. It can measure the accuracy of inference algorithms when the data resources evolve. Using a uniform pipeline, the framework can orchestrate multiple runs using different data, corpora and variant prioritization tools, organizing it into a grid configuration, testing all combinations and generating analysis outputs for each of them. As an example, these reports may be useful to ontology developers, who can gain insight and understand the effects of their proposed changes on different phenotype-matching approaches, and by extension, the performance of tools that use these to perform functions such as variant prioritisation. PhEval's integrated workflow can facilitate the variant prioritisation algorithms benchmark process by bundling multiple steps into a pipeline and generating standardised results.
  59. Sugar, Spice, and Everything Nice: Curating glycan and glycan adjacent data
    Luc Thomès, Jon Lundstrøm, James Urban, Daniel Bojar
    Expand the abstract
    Glycans are biomolecules made of linked sugars and play a crucial role in various biological processes including symbiosis, inflammation, and immune signalling. Recently, there has been a rise in availability of large scale glycomics data, requiring both improved curation standards along with modern bioinformatic techniques. This has coincided with an expansion of deep learning libraries and computational power. While many glycan resources exist, there remains a number of untouched data found in individual publications which is challenging to extract programmatically. Even when accessible via an API, this data is still often decentralised, found in differing formats, and are not systematically curated. We here describe a group of four curated glycan bioinformatics resources, covering a number of subfields. These include, glycan biological contexts, milk oligosaccharide biosynthesis, glycan-lectin interactions, and tandem mass spectrometry. Our focus was on simplified access, ongoing maintenance efforts, and potential deep learning applications. Machine learning methods were applied to each novel dataset presented to extract valuable biological insights, indicating high quality data. We envision these resources as an entry point for other researchers to use and contribute to.