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  ABOUT

  We are excited to introduce XGR-model (XGRm), a model organism version of our highly popular XGR software. XGRm is a web server designed to provide real-time enrichment and subnetwork analyses for user-provided lists of genes or genomic regions, now focusing on the mouse organism. It achieves this by harnessing the power of ontologies, networks, and genomic datasets (including genomic proximity and enhancer-promoter interactions).

  In genomics research, a significant challenge lies in bridging the gap between generated genomic summary data and meaningful knowledge discovery. We define genomic summary data as a list of genes (more generally, genomic regions), accompanied by their significance levels (e.g. p-values). Our commitment to addressing this challenge has led us to develop efficient and effective approaches and tools for deciphering genomic summary data. These tools and approaches (such as dnet, XGR, Pi, OpenXGR) can be found in the section PUBLICATIONS below.

  XGRm is a natural extension of our prior work, harnessing the growing wealth of ontologies and networks in the field. It boasts four distinct analysers, each tailored to interpret genomic summary data at the gene or genomic region level. Two enrichment analysers are to identify enriched ontology term, and two subnetwork analysers for identifying gene subnetworks. These analysers are described below:
  •  GElyser, Gene-centric Enrichment analyser that identifies enriched ontology terms from input gene list;
  •  RElyser, Region-centric Enrichment analyser that identifies enriched ontology terms for genes linked from input genomic region list;
  •  GSlyser, Gene-centric Subnetwork analyser that identifies a gene subnetwork based on input gene-level summary data;
  •  RSlyser, Region-centric Subnetwork analyser that identifies a gene subnetwork based on genes linked from input genomic region-level summary data.

  FUNDING

    National Natural Science Foundation of China (32170663 & 82300263)

    Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning

    Science and Technology Innovation Key R&D Program of Chongqing (CSTB2023TIAD-STX0001)

    National Key R&D Program of China (2022YFA1103300)

    Innovative Research Team of High-Level Local Universities in Shanghai

  PUBLICATIONS

    The 'dnet' approach promotes emerging research on cancer patient survival

    Genome Medicine 2014

    DOI: 10.1186/s13073-014-0064-8

    XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits

    Genome Medicine 2016

    DOI: 10.1186/s13073-016-0384-y

    A genetics-led approach defines the drug target landscape of 30 immune-related traits

    Nature Genetics 2019

    DOI: 10.1038/s41588-019-0456-1

    Priority index: database of genetic targets in immune-mediated disease

    Nucleic Acids Research 2022

    DOI: 10.1093/nar/gkab994

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

    Nucleic Acids Research 2023

    DOI: 10.1093/nar/gkad357