{"doi":"10.1093/nar/gks438","title":"Immune epitope database analysis resource","abstract":"The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.","journal":"Nucleic Acids Research","year":2012,"id":11900,"datarank":7.2904478607613905,"base_score":6.257667587882639,"endowment":6.257667587882639,"self_citation_contribution":0.938650138182396,"citation_network_contribution":6.351797722578994,"self_endowment_contribution":0.938650138182396,"citer_contribution":6.351797722578994,"corpus_percentile":97.60191846522781,"corpus_rank":311,"citation_count":521,"citer_count":100,"citers_with_citation_signal":100,"citers_with_endowment":100,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9019,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2012-05-18","fair_score":44.5833,"fair_percentile":32.89183222958057,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":56538,"name":"Z. 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Peters","orcid":null,"position":11,"is_corresponding":false},{"id":56541,"name":"Young‐Hee Kim","orcid":"0000-0002-1111-632X","position":12,"is_corresponding":false},{"id":55663,"name":"Julia Ponomarenko","orcid":"0000-0002-1477-9444","position":13,"is_corresponding":false},{"id":56547,"name":"Zhenggang Zhu","orcid":"0000-0002-4800-8082","position":14,"is_corresponding":false},{"id":95418,"name":"David Tamang","orcid":null,"position":15,"is_corresponding":false},{"id":56540,"name":"Peng Wang","orcid":"0000-0003-1845-7589","position":16,"is_corresponding":false},{"id":19878,"name":"Jason Greenbaum","orcid":"0000-0002-1381-0390","position":17,"is_corresponding":false},{"id":55670,"name":"Alessandro Sette","orcid":"0000-0001-7013-2250","position":18,"is_corresponding":false},{"id":56545,"name":"Ole Lund","orcid":"0000-0003-1108-0491","position":19,"is_corresponding":false},{"id":125,"name":"Philip  E. Bourne","orcid":"0000-0002-7618-7292","position":20,"is_corresponding":false},{"id":56546,"name":"Morten Nielsen","orcid":"0000-0001-7885-4311","position":21,"is_corresponding":false},{"id":56548,"name":"Björn Peters","orcid":"0000-0002-2941-1335","position":22,"is_corresponding":false},{"id":19142,"name":"Y. Kim","orcid":null,"position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":"22610854","pmcid":"PMC3394288","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"gold","license":"cc-by-nc","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":52.5,"fair_a":67.5,"fair_i":25.0,"fair_r":33.3333,"fair_zscore":-0.4132,"fair_rationale":{"fair_score":44.58,"has_llm":true,"dimensions":{"F":{"name":"Findable","score":52.5,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"datacite=0, pmcid=True, pmid=True","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers (OpenAlex)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no OpenAlex id","rationale":null},{"key":"f_metadata_richness","label":"Rich, machine-readable metadata","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"The paper provides a narrative description of the IEDB-AR tools and updates but lacks explicit mention of machine-readable metadata (e.g., structured metadata, ontologies, or standardized identifiers) for the data or tools."}]},"A":{"name":"Accessible","score":67.5,"criteria":[{"key":"a_open_access","label":"Open Access / files deposited","kind":"deterministic","weight":1.5,"fraction":1.0,"signal":"Open Access","rationale":null},{"key":"a_retrievable","label":"Free full text retrievable","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"0 OA location(s)","rationale":null},{"key":"a_access_protocol","label":"Clear data/code access protocol","kind":"llm","weight":1.0,"fraction":0.75,"signal":null,"rationale":"The paper clearly describes web access, API (RESTful HTTP), standalone packages (tarball/Ubuntu packages), and virtual machine images for IEDB-AR, making the tools accessible via multiple protocols."}]},"I":{"name":"Interoperable","score":25.0,"criteria":[{"key":"i_linked_data","label":"Linked datasets / DataCite relations","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"linked_datasets=0, datacite=0","rationale":null},{"key":"i_standard_ids","label":"References data via standard accessions","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"accessions=0, trials=0","rationale":null},{"key":"i_standards","label":"Standard formats, vocabularies & identifiers","kind":"llm","weight":1.0,"fraction":0.5,"signal":null,"rationale":"The paper uses standard bioinformatics formats (e.g., FASTA, PDB, ClustalW) and refers to standard identifiers (MHC alleles, PDB IDs), but does not explicitly state use of machine-readable controlled vocabularies or community standards for interoperability."}]},"R":{"name":"Reusable","score":33.33,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.0,"signal":"no license","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"is_dataset","rationale":null},{"key":"r_reusability","label":"Data-availability statement, license & reproducibility","kind":"llm","weight":2.0,"fraction":0.5,"signal":null,"rationale":"The paper states an open-access license (Creative Commons BY-NC 3.0) and provides downloadable tools for non-commercial use, but lacks a formal data-availability statement for the underlying datasets and detailed reproducibility instructions."}]}},"suggestions":["Add explicit references to machine-readable metadata (e.g., schema.org, BioSchemas) for the IEDB-AR tools and datasets to improve findability.","Include a dedicated data-availability statement describing how to access the training/validation datasets and their formats.","Specify the use of controlled vocabularies (e.g., MIABIS, OBO Foundry) for epitope and MHC allele annotations to enhance interoperability.","Provide a license/DOI for the software packages and a reproducibility guide (e.g., containerized execution with versioned dependencies)."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v2","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v2","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-06-18T00:34:25.679896Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}