{"doi":"10.1093/nar/gkae931","title":"scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.</jats:p>","journal":"Nucleic Acids Research","year":2025,"id":640030,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":47.7,"corpus_rank":7057,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1663285,"name":"Qiheng Qian","orcid":"0000-0002-1930-7171","position":1,"is_corresponding":false},{"id":1471408,"name":"Hao Gao","orcid":"0000-0002-6139-925X","position":2,"is_corresponding":false},{"id":1519044,"name":"Zhuojing Fan","orcid":"0009-0003-3575-6967","position":3,"is_corresponding":false},{"id":1663287,"name":"Jingyao Zeng","orcid":"0000-0001-7364-9677","position":4,"is_corresponding":false},{"id":1519045,"name":"Jingfa Xiao","orcid":"0000-0002-2835-4340","position":5,"is_corresponding":false},{"id":1663283,"name":"Jialin Mai","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.</jats:p>","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"39420631","pmcid":"PMC11701648","openalex_id":"https://openalex.org/W4403519221","authors":[],"funders":[{"funder_name":"Chinese Academy of Sciences","grant_id":"XDB38030400","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"32170669","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"32300542","title":null},{"funder_name":"National Key Research Program of China","grant_id":"2020YFA0907001","title":null},{"funder_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","grant_id":"2022098","title":null}],"total_grants":5,"fwci":1.2925,"citation_percentile":0.79672268,"influential_citations":0,"citation_trend":[{"year":2025,"count":4},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by-nc","oa_locations":[{"url":"https://doi.org/10.1093/nar/gkae931","host_type":"journal"},{"url":"https://doi.org/10.1093/nar/gkae931","host_type":"publisher"},{"url":"https://academic.oup.com/nar/article-pdf/53/D1/D1195/59843729/gkae931.pdf","host_type":"publisher"},{"url":"https://pubmed.ncbi.nlm.nih.gov/39420631","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11701648","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11701648/pdf/gkae931.pdf","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11701648","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11701648?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Single-cell and spatial transcriptomics","Bioinformatics and Genomic Networks","Epigenetics and DNA Methylation","Single-Cell Analysis","Humans","Transcriptome","Databases, Genetic","Quantitative Trait Loci","Knowledge Bases","Genome-Wide Association Study","Gene Expression Profiling","Software","Gene Regulatory Networks"],"mesh_terms":["Humans","Software","Gene Expression Profiling","Databases, Genetic","Quantitative Trait Loci","Knowledge Bases","Gene Regulatory Networks","Genome-Wide Association Study","Single-Cell Analysis","Transcriptome"],"keywords":["Expression quantitative trait loci","Biology","Atlas (anatomy)","Computational biology","Upload","Genome-wide association study","Transcriptome","Gene regulatory network","Computer science","Gene","Genetics","Gene expression","World Wide Web"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T05:53:29.540398Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}