探索系统性红斑狼疮血液转录组多样性的交互式网络应用程序。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Eléonore Bettacchioli, Laurent Chiche, Damien Chaussabel, Divi Cornec, Noémie Jourde-Chiche, Darawan Rinchai
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引用次数: 0

摘要

在系统性红斑狼疮(SLE)等复杂的自身免疫性疾病领域,系统免疫学方法已被证明在转化研究中非常有价值。大规模的转录组剖析数据集已被收集起来,并在公共资料库中向研究界提供,但主流研究人员仍然很难访问和使用这些数据集。方便研究人员与大规模数据集互动的工具和技术(如用户友好型网络应用程序)可以促进数据的再利用和知识的发现。来自 LUPUCE 队列的微阵列血液转录组数据(可在基因表达总库(GSE49454)上公开获取)包括来自 62 名成年系统性红斑狼疮患者的 157 份样本。这些特征良好的样本对应不同程度的疾病活动、不同类型的复发(包括活检证实的狼疮性肾炎)、不同的自身抗体特征和不同程度的干扰素特征。我们部署了一个网络应用程序来展示 LUPUCE 数据集的总体级、模块级和基因级分析结果。用户可以探索系统性红斑狼疮样本的相似性和异质性,浏览不同层次的分析结果,测试假设并生成定制的指纹图谱和热图,这些结果可用于报告或手稿。该资源可通过以下链接获取:https://immunology-research.shinyapps.io/LUPUCE/。该网络应用程序可作为独立的资源,用于探索系统性红斑狼疮血液转录本特征的变化及其与临床和免疫学参数的关系,从而提出新的研究假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An interactive web application for exploring systemic lupus erythematosus blood transcriptomic diversity.

In the field of complex autoimmune diseases such as systemic lupus erythematosus (SLE), systems immunology approaches have proven invaluable in translational research settings. Large-scale datasets of transcriptome profiling have been collected and made available to the research community in public repositories, but remain poorly accessible and usable by mainstream researchers. Enabling tools and technologies facilitating investigators' interaction with large-scale datasets such as user-friendly web applications could promote data reuse and foster knowledge discovery. Microarray blood transcriptomic data from the LUPUCE cohort (publicly available on Gene Expression Omnibus, GSE49454), which comprised 157 samples from 62 adult SLE patients, were analyzed with the third-generation (BloodGen3) module repertoire framework, which comprises modules and module aggregates. These well-characterized samples corresponded to different levels of disease activity, different types of flares (including biopsy-proven lupus nephritis), different auto-antibody profiles and different levels of interferon signatures. A web application was deployed to present the aggregate-level, module-level and gene-level analysis results from LUPUCE dataset. Users can explore the similarities and heterogeneity of SLE samples, navigate through different levels of analysis, test hypotheses and generate custom fingerprint grids and heatmaps, which may be used in reports or manuscripts. This resource is available via this link: https://immunology-research.shinyapps.io/LUPUCE/. This web application can be employed as a stand-alone resource to explore changes in blood transcript profiles in SLE, and their relation to clinical and immunological parameters, to generate new research hypotheses.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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