Matrisome AnalyzeR: A suite of tools to annotate and quantify ECM molecules in big datasets across organisms.

Matrisome AnalyzeR: A suite of tools to annotate and quantify ECM molecules in big datasets across organisms.
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Matrisome AnalyzeR:一套用于注释和量化跨生物体大数据集中的 ECM 分子的工具。

DOI:
10.1101/2023.04.18.537378
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发表时间:
2023
期刊:
bioRxiv : the preprint server for biology
影响因子:
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通讯作者:
Naba,Alexandra
Naba,Alexandra
中科院分区:
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文献类型:
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作者:
Petrov,PetarB;Considine,JamesM;Izzi,Valerio;Naba,Alexandra

文献摘要

相似文献

细胞外基质(ECM)是一种复杂的蛋白质网络,构成了多细胞生物中所有组织的支架。它在生活的各个方面都起着至关重要的作用,从协调细胞在发育过程中的迁移,到支持组织修复。它在疾病的病因或进展中也起着关键作用。为了研究这个区室,我们之前已经定义了多种生物编码ECM和ECM相关蛋白的所有基因的纲要。我们将这个纲要称为“基质体”,并将基质成分进一步分类为不同的结构或功能类别。这种命名法现在被研究界广泛采用来注释“组学”数据集,并为推进基础和转化ECM研究做出了贡献。在这里,我们报告Matrisome AnalyzeR的开发,这是一套工具,包括一个基于web的应用程序和一个R包。任何有兴趣在大型数据集中注释、分类和制表基质分子的人都可以使用这个web应用程序,而不需要编程知识。配套的R包可供更有经验的用户使用,他们对处理更大的数据集或其他数据可视化选项感兴趣。
The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays crucial roles in all aspects of life–from orchestrating cell migration during development, to supporting tissue repair. It also plays critical roles in the etiology or progression of diseases. To study this compartment, we have previously defined the compendium of all genes encoding ECM and ECM-associated proteins for multiple organisms. We termed this compendium the ‘matrisome’and further classified matrisome components into different structural or functional categories. This nomenclature is now largely adopted by the research community to annotate ‘-omics’ datasets and has contributed to advance both fundamental and translational ECM research. Here, we report the development of Matrisome AnalyzeR, a suite of tools including a web-based application and an R package. The web application can be used by anyone interested in annotating, classifying and tabulating matrisome molecules in large datasets without requiring programming knowledge. The companion R package is available to more experienced users, interested in processing larger datasets or in additional data visualization options.