OBJECTIVES: Federated Ecosystems for Analytics and Standardized Technologies (FEAST) is a modular, cloud-based platform developed through the ARPA-H Biomedical Data Fabric initiative to enable secure, federated analysis of real-world biomedical data. To guide and iteratively refine its modular design, the FEAST team conducted a cross-institutional survey to systematically identify and prioritize research needs related to authorized-access data across diverse biomedical domains. This study presents a structured synthesis of submitted use cases to uncover infrastructure gaps, data integration challenges, and translational opportunities. The results from the survey inform both front-end user-facing functionality and backend data requirements, shaping how the interface supports user interactions, data types, and compliance with security and interoperability standards. MATERIALS AND METHODS: A structured survey form was distributed to researchers affiliated with participating institutions, including DNA-HIVE, The George Washington University (GW-FEAST), Weill Cornell Medicine, Vanderbilt University Medical Center, Georgetown University, European Bioinformatics Institute, and Kaiser Permanente. Respondents completed standardized fields describing the data types of interest, project goals, analytic methods, and perceived technical barriers. The collected responses were curated and analyzed to identify common needs related to privacy, interoperability, scalability, and workflow reproducibility. RESULTS: The survey compiled 61 use cases spanning genomics, imaging, clinical phenotyping, EHR-driven analytics, and precision medicine. Common themes included the need for multi-modal data integration, HL7 FHIR-based secure access, federated model training without PII retention, and containerized microservices for scalable deployment. Convergent needs across institutions emphasized consistent demand for FAIR-compliant infrastructure and readiness for real-world data analytics. CONCLUSION: The FEAST Use Cases survey provides a cross-sectional view of biomedical informatics priorities grounded in real-world data needs. The findings offer a strategic blueprint for developing federated, privacy-preserving infrastructure to support secure, collaborative, and scalable biomedical research.

MOTIVATION: To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. RESULTS: We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. AVAILABILITY AND IMPLEMENTATION: Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. RESULTS: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. AVAILABILITY AND IMPLEMENTATION: http://proteininformationresource.org/rps/viruses/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. Results: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation. Availability and implementation: Web access and file download from UniProt website at http://www.uniprot.org/uniref and ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. BLAST searches against UniRef are available at http://www.uniprot.org/blast/ Contact: [email protected]

In this paper, we attempt to build a pipeline that identifies and extracts disease-drug relationships via sentence classification, and demonstrate the feasibility and utility of our approach using tardive dyskinesia as a case study. We manually developed and annotated a biomedicai training corpus for tardive dyskinesia. Using 10-fold cross validation, we tested and trained a naïve Bayes classifier to identify sentences pertaining to disease-drug relationships. Our precision, recall, and F-measure were all approximately 66%, and area under the ROC curve was over 80%. Our method helps to elucidate various drug effects on tardive dyskinesia and constitutes an initial effort toward the task of disease-drug relationship extraction.

Quality control and harmonization of data is a vital and challenging undertaking for any successful data coordination center and a responsibility shared between the multiple sites that produce, integrate, and utilize the data. Here we describe a coordinated effort between scientists and data managers in the Cancer Family Registries to implement a data governance infrastructure consisting of both organizational and technical solutions. The technical solution uses a rule-based validation system that facilitates error detection and correction for data centers submitting data to a central informatics database. Validation rules comprise both standard checks on allowable values and a crosscheck of related database elements for logical and scientific consistency. Evaluation over a 2-year timeframe showed a significant decrease in the number of errors in the database and a concurrent increase in data consistency and accuracy.

MOTIVATION: Identifier (ID) mapping establishes links between various biological databases and is an essential first step for molecular data integration and functional annotation. ID mapping allows diverse molecular data on genes and proteins to be combined and mapped to functional pathways and ontologies. We have developed comprehensive protein-centric ID mapping services providing mappings for 90 IDs derived from databases on genes, proteins, pathways, diseases, structures, protein families, protein interaction, literature, ontologies, etc. The services are widely used and have been regularly updated since 2006. AVAILABILITY: www.uniprot.org/mappingandproteininformation-resource.org/pirwww/search/idmapping.shtml CONTACT: [email protected].

BACKGROUND: The UniProt consortium was formed in 2002 by groups from the Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI) and the Protein Information Resource (PIR) at Georgetown University, and soon afterwards the website http://www.uniprot.org was set up as a central entry point to UniProt resources. Requests to this address were redirected to one of the three organisations' websites. While these sites shared a set of static pages with general information about UniProt, their pages for searching and viewing data were different. To provide users with a consistent view and to cut the cost of maintaining three separate sites, the consortium decided to develop a common website for UniProt. Following several years of intense development and a year of public beta testing, the http://www.uniprot.org domain was switched to the newly developed site described in this paper in July 2008. DESCRIPTION: The UniProt consortium is the main provider of protein sequence and annotation data for much of the life sciences community. The http://www.uniprot.org website is the primary access point to this data and to documentation and basic tools for the data. These tools include full text and field-based text search, similarity search, multiple sequence alignment, batch retrieval and database identifier mapping. This paper discusses the design and implementation of the new website, which was released in July 2008, and shows how it improves data access for users with different levels of experience, as well as to machines for programmatic access.http://www.uniprot.org/ is open for both academic and commercial use. The site was built with open source tools and libraries. Feedback is very welcome and should be sent to [email protected]. CONCLUSION: The new UniProt website makes accessing and understanding UniProt easier than ever. The two main lessons learned are that getting the basics right for such a data provider website has huge benefits, but is not trivial and easy to underestimate, and that there is no substitute for using empirical data throughout the development process to decide on what is and what is not working for your users.

MOTIVATION: Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. RESULTS: The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. AVAILABILITY: UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: The Protein Information Resource (PIR) has greatly expanded its Web site and developed a set of interactive search and analysis tools to facilitate the analysis, annotation, and functional identification of proteins. New search engines have been implemented to combine sequence similarity search results with database annotation information. The new PIR search systems have proved very useful in providing enriched functional annotation of protein sequences, determining protein superfamily-domain relationships, and detecting annotation errors in genomic database archives. AVAILABILITY: http://pir.georgetown.edu/. CONTACT: [email protected]