Data sources
Sugi Atlas builds on top of BioBTree, which integrates the 70 primary biomedical databases listed below. Each row shows the source and its citing paper.
Acknowledgments
Every page in Sugi Atlas rests on the sustained work of the curators, researchers, and engineers behind the databases listed below. Their long-term commitment to open, structured, citable biomedical data is what makes Sugi Atlas possible.
Data freshness
Sugi Atlas follows BioBTree's update cycle. BioBTree's pipeline processes the full federation efficiently, so the atlas is refreshed against the latest snapshot at least monthly — and more frequently when a Sugi Atlas improvement triggers a rebuild.
Additional per-source statistics will be added here in the future.
Datasets 70
| Source | Reference |
|---|---|
| AlphaFold | Varadi et al., NAR 2022 |
| AlphaMissense | Cheng et al., Science 2023 |
| Antibody db | TheraSAbDab + SAbDab + IMGT (Raybould 2020; Dunbar 2014; Giudicelli 2005) |
| BAO | Abeyruwan et al., 2014 |
| Bgee | Bastian et al., NAR 2021 |
| BindingDB | Liu et al., NAR 2025 |
| BioGRID | Oughtred et al., 2019 |
| BRENDA | Hauenstein et al., NAR 2026 |
| Cellosaurus | Bairoch, J Biomol Tech 2018 |
| CellPhoneDB | Troule et al., 2025 |
| ChEBI | Degtyarenko et al., NAR 2008 |
| chembl_document | Zdrazil et al., NAR 2024 |
| CIViC | Griffith et al., Nat Genet 2017 |
| CL | Diehl et al., 2016 |
| ClinGen Gene-Disease Validity | Rehm et al., NEJM 2015 |
| Clinical Trials | Zarin et al., 2011 |
| ClinVar | Landrum et al., NAR 2014 |
| CollecTRI | Müller-Dott et al., 2023 |
| CORUM | Tsitsiridis et al., NAR 2023 |
| CTD | Davis et al., 2025 |
| CZ CELLxGENE | CZI Cell Science Program, 2025 |
| dbSNP | Sherry et al., NAR 2001 |
| DepMap | Tsherniak et al., Cell 2017 |
| DIAMOND Protein Similarity | Buchfink et al., 2021 |
| ECO | Nadendla et al., NAR 2022 |
| EFO | Malone et al., 2010 |
| ENCODE cCRE | Moore et al., Nature 2020 |
| Ensembl | Dyer et al., NAR 2025 |
| ESM2 Protein Similarity | Lin et al., Science 2023 |
| FANTOM5 Promoter | Nobusada et al., 2025 |
| GenCC | DiStefano et al., 2022 |
| GeneRIF | Brown et al., NAR 2015 |
| GO | Ashburner et al., Nat Genet 2000 |
| GtoPdb | Harding et al., NAR (IUPHAR/BPS) |
| GWAS Study | Sollis et al., NAR 2023 |
| HGNC | Seal et al., NAR 2023 |
| hmdb | Wishart et al., NAR 2022 |
| HPO | Gargano et al., NAR 2024 |
| IntAct | del Toro et al., 2022 |
| Interpro | Blum et al., NAR 2025 |
| intOGen | Martínez-Jiménez et al., Nat Rev Cancer 2020 |
| JASPAR | Rauluseviciute et al., NAR 2024 |
| LIPID MAPS | Sud et al., NAR 2007 |
| literature_mappings | NCBI PMC PMID/PMCID/DOI mapping (NCBI/NLM) |
| MeSH | Lipscomb, 2000 |
| miRDB | Chen & Wang, NAR 2020 |
| MONDO | Vasilevsky et al., 2025 |
| MSigDB | Liberzon et al., 2015 |
| NCBI/Entrez Gene | Maglott et al., NAR 2011 |
| OBA | Stefancsik et al., Database (Oxford) 2023 |
| OBI | Bandrowski et al., PLoS ONE 2016 |
| Orphanet | Rath et al., 2012 |
| Patent | Papadatos et al., NAR 2016 |
| PATO | Gkoutos et al., Brief Bioinform 2018 |
| PharmGKB | Gong et al., 2021 |
| Protein Data Bank | Armstrong et al., NAR 2020 |
| PubChem BioActivity | Kim et al., NAR 2025 |
| Reactome | Milacic et al., NAR 2024 |
| RefSeq | O’Leary et al., NAR 2016 |
| Rhea | Bansal et al., 2022 |
| RNAcentral | RNAcentral Consortium, NAR 2026 |
| SC Expression Atlas | Papatheodorou et al., NAR 2020 |
| SIGNOR | Lo Surdo et al., 2026 |
| SpliceAI | Jaganathan et al., Cell 2019 |
| STRING | Szklarczyk et al., NAR 2023 |
| SwissLipids | Aimo et al., 2015 |
| Taxonomy | Federhen, NAR 2012 |
| UBERON | Mungall et al., 2012 |
| Uniprot | UniProt Consortium, NAR 2025 |
| XCO | Smith et al., RGD / Mamm Genome |