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iSMOD:基于图像的单细胞多组学数据的综合浏览器。

iSMOD: an integrative browser for image-based single-cell multi-omics data.

机构信息

Department of Automation, Tsinghua University, Beijing 100084, China.

Institute of Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China.

出版信息

Nucleic Acids Res. 2023 Sep 8;51(16):8348-8366. doi: 10.1093/nar/gkad580.

Abstract

Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.

摘要

基因组和转录组图像数据分别由 DNA 和 RNA 荧光原位杂交(FISH)表示,以及蛋白质组数据,特别是与核蛋白相关的数据,可以帮助阐明与染色质、信使 RNA 和关键蛋白的空间位置有关的基因调控。然而,缺乏基于图像的多组学数据采集和分析方法。为此,我们旨在开发第一个集成浏览器,称为 iSMOD(基于图像的单细胞多组学数据库),以根据标题、摘要和相关实验图来收集和浏览综合的 FISH 和核蛋白质组学数据,该浏览器集成了 20000+(仍在不断增加)篇已发表文献中聚焦于细胞核关键因子的多组学研究。我们还提供了几个示例演示,展示了 iSMOD 的广泛应用,包括对疾病分子靶标的多组学研究;利用多组学相互作用探索生物现象背后的工作机制,以及在虚拟细胞核中整合 3D 多组学数据。iSMOD 是描绘相关研究全局视图的基石,能够整合分散的数据,从而提供关于分子途径机制缺失成分的新见解,并有助于改进和高效的科学研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfbf/10484677/2830e3ceae1a/gkad580figgra1.jpg

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