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

Chaohui Bao, Shan Wang, Lulu Jiang, Zhongcheng Fang, Kexin Zou, James Lin, Saijuan Chen, Hai Fang*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)

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.
Original languageEnglish
Article numbergkad357
Pages (from-to)W387-W396
Number of pages10
JournalNucleic Acids Research
Volume51
Issue numberW1
DOIs
Publication statusPublished - 9 May 2023

Bibliographical note

Funding Information:
National Natural Science Foundation of China [32170663 to H.F.]; Shanghai Pujiang Program [21PJ1409600 to H.F.]; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [H.F.]; Innovative Research Team of High-Level Local Universities in Shanghai. Funding for open access charge: National Natural Science Foundation of China.

Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.

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