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OpenXGR: a web-server update for genomic summary data interpretation.
Bao, Chaohui; Wang, Shan; Jiang, Lulu; Fang, Zhongcheng; Zou, Kexin; Lin, James; Chen, Saijuan; Fang, Hai.
Afiliação
  • Bao C; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai200025, China.
  • Wang S; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai200025, China.
  • Jiang L; Translational Health Sciences, University of Bristol, BristolBS1 3NY, UK.
  • Fang Z; Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai200092, China.
  • Zou K; School of Life Sciences, Central South University, Hunan410083, China.
  • Lin J; High Performance Computing Center, Shanghai Jiao Tong University, Shanghai200240, China.
  • Chen S; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai200025, China.
  • Fang H; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai200025, China.
Nucleic Acids Res ; 51(W1): W387-W396, 2023 07 05.
Article em En | MEDLINE | ID: mdl-37158276
How to effectively convert genomic summary data into downstream knowledge discovery represents a major challenge in human genomics research. To address this challenge, we have developed efficient and effective approaches and tools. Extending our previously established software tools, we here introduce OpenXGR (http://www.openxgr.com), a newly designed web server that offers almost real-time enrichment and subnetwork analyses for a user-input list of genes, SNPs or genomic regions. It achieves so through leveraging ontologies, networks, and functional genomic datasets (such as promoter capture Hi-C, e/pQTL and enhancer-gene maps for linking SNPs or genomic regions to candidate genes). Six analysers are provided, each doing specific interpretations tailored to genomic summary data at various levels. Three enrichment analysers are designed to identify ontology terms enriched for input genes, as well as genes linked from input SNPs or genomic regions. Three subnetwork analysers allow users to identify gene subnetworks from input gene-, SNP- or genomic region-level summary data. With a step-by-step user manual, OpenXGR provides a user-friendly and all-in-one platform for interpreting summary data on the human genome, enabling more integrated and effective knowledge discovery.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Genômica Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Genômica Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China