XGRm: A Web Server for Interpreting Mouse Summary-level Genomic Data

IF 4.7 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Journal of Molecular Biology Pub Date : 2024-09-01 DOI:10.1016/j.jmb.2024.168705

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Abstract

We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms. Currently, it hosts two enrichment analysers and two subnetwork analysers to support enrichment and subnetwork analyses for user-input mouse genomic data, whether gene-centric or genomic region-centric. The enrichment analysers identify ontology term enrichments for input genes (GElyser) or for genes linked from input genomic regions (RElyser). The subnetwork analysers rely on our previously established network algorithm to identify gene subnetworks from input gene-centric summary data (GSlyser) or from input region-centric summary data (RSlyser), leveraging network information about either functional interactions or pathway-derived interactions. Collectively, XGRm offers an all-in-one solution for gaining systems biology insights into summary-level genomic data in mice, underpinned by our commitment to regular updates as well as natural extensions to other model organisms.

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XGRm：解读小鼠摘要级基因组数据的网络服务器

我们介绍 XGR-model（或 XGRm），这是一个可在 http://www.xgrm.pro 上访问的网络服务器，旨在满足日益增长的有效解释模式生物基因组数据的需求。目前，它拥有两个富集分析器和两个子网络分析器，支持对用户输入的小鼠基因组数据（无论是以基因为中心还是以基因组区域为中心）进行富集和子网络分析。富集分析器可识别输入基因（GElyser）或输入基因组区域链接基因（RElyser）的本体术语富集。子网络分析器依赖于我们之前建立的网络算法，利用有关功能相互作用或通路衍生相互作用的网络信息，从以输入基因为中心的汇总数据（GSlyser）或以输入区域为中心的汇总数据（RSlyser）中识别基因子网络。总之，XGRm 提供了一个一体化的解决方案，让我们可以从小鼠基因组摘要数据中获得系统生物学的见解，我们承诺定期更新，并自然扩展到其他模式生物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Molecular Biology 生物-生化与分子生物学

CiteScore

11.30

自引率

1.80%

发文量

412

审稿时长

28 days

期刊介绍： Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions. Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.