OpenXGR: a web-server update for genomic summary data interpretation

Author:

Bao Chaohui1,Wang Shan1,Jiang Lulu2,Fang Zhongcheng3,Zou Kexin4,Lin James5,Chen Saijuan1,Fang Hai1ORCID

Affiliation:

1. 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 , Shanghai 200025 , China

2. Translational Health Sciences, University of Bristol , Bristol BS1 3NY , UK

3. Bioinformatics Department, School of Life Sciences and Technology, Tongji University , Shanghai 200092 , China

4. School of Life Sciences, Central South University , Hunan 410083 , China

5. High Performance Computing Center, Shanghai Jiao Tong University , Shanghai 200240 , China

Abstract

Abstract 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.

Funder

National Natural Science Foundation of China

Shanghai Pujiang Program

Shanghai Institutions of Higher Learning

Publisher

Oxford University Press (OUP)

Subject

Genetics

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