{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:3BCMFPKDPLTNJASLZZZ6QR2725","short_pith_number":"pith:3BCMFPKD","schema_version":"1.0","canonical_sha256":"d844c2bd437ae6d4824bce73e8475fd74fd6eea68418ee7df83b6f5db6b122af","source":{"kind":"arxiv","id":"1404.4482","version":1},"attestation_state":"computed","paper":{"title":"VSEAMS: A pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"q-bio.GN","authors_text":"Chris Wallace, Hui Guo, Oliver S Burren","submitted_at":"2014-04-17T11:02:03Z","abstract_excerpt":"Motivation: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (p values) and functional genomic datasets should help to elucidate mechanisms. Results: We describe the extension of a previously described non-parametric method to test whether GWAS signals are enriched in functionally defined loci to a situation where only GWAS p values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to integrate functional gene sets defined via transcription factor"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1404.4482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.GN","submitted_at":"2014-04-17T11:02:03Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"ac726d734a1b2cdc7f208f46ffe0f89924de7e6449e0808409c69b13a865b9a2","abstract_canon_sha256":"25a9fa4311ad830201e01e191c70322cd4e0c86ba4ce1f82c5cf71a56f859c0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:01.908362Z","signature_b64":"dY89V/Kqvihrw9WNhZyi2/0EUPVyy/EPLFSrSb3m9HLQcTi6Rc2U6XzRXY1HgliurNTIaDVHcxej/Y0GH5u8Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d844c2bd437ae6d4824bce73e8475fd74fd6eea68418ee7df83b6f5db6b122af","last_reissued_at":"2026-05-18T02:40:01.907802Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:01.907802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VSEAMS: A pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"q-bio.GN","authors_text":"Chris Wallace, Hui Guo, Oliver S Burren","submitted_at":"2014-04-17T11:02:03Z","abstract_excerpt":"Motivation: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (p values) and functional genomic datasets should help to elucidate mechanisms. Results: We describe the extension of a previously described non-parametric method to test whether GWAS signals are enriched in functionally defined loci to a situation where only GWAS p values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to integrate functional gene sets defined via transcription factor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.4482","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1404.4482","created_at":"2026-05-18T02:40:01.907876+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.4482v1","created_at":"2026-05-18T02:40:01.907876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.4482","created_at":"2026-05-18T02:40:01.907876+00:00"},{"alias_kind":"pith_short_12","alias_value":"3BCMFPKDPLTN","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_16","alias_value":"3BCMFPKDPLTNJASL","created_at":"2026-05-18T12:28:11.866339+00:00"},{"alias_kind":"pith_short_8","alias_value":"3BCMFPKD","created_at":"2026-05-18T12:28:11.866339+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725","json":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725.json","graph_json":"https://pith.science/api/pith-number/3BCMFPKDPLTNJASLZZZ6QR2725/graph.json","events_json":"https://pith.science/api/pith-number/3BCMFPKDPLTNJASLZZZ6QR2725/events.json","paper":"https://pith.science/paper/3BCMFPKD"},"agent_actions":{"view_html":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725","download_json":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725.json","view_paper":"https://pith.science/paper/3BCMFPKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.4482&json=true","fetch_graph":"https://pith.science/api/pith-number/3BCMFPKDPLTNJASLZZZ6QR2725/graph.json","fetch_events":"https://pith.science/api/pith-number/3BCMFPKDPLTNJASLZZZ6QR2725/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725/action/storage_attestation","attest_author":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725/action/author_attestation","sign_citation":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725/action/citation_signature","submit_replication":"https://pith.science/pith/3BCMFPKDPLTNJASLZZZ6QR2725/action/replication_record"}},"created_at":"2026-05-18T02:40:01.907876+00:00","updated_at":"2026-05-18T02:40:01.907876+00:00"}