Article: Matrisome AnalyzeR: A suite of tools to annotate and quantify ECM molecules in big datasets across organisms.
bioRxiv : the preprint server for biology
2023
Abstract: The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays critical roles in all aspects of life: from orchestrating cell migration during development, to supporting tissue ...
Abstract | The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays critical 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 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 ( https://sites.google.com/uic.edu/matrisome/tools/matrisome-analyzer ) and an R package ( https://github.com/Matrisome/MatrisomeAnalyzeR ). 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. Summary statement: Matrisome AnalyzeR is a suite of tools, including a web-based app and an R package, designed to facilitate the annotation and quantification of extracellular matrix components in big datasets. |
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Language | English |
Publishing date | 2023-04-20 |
Publishing country | United States |
Document type | Preprint |
DOI | 10.1101/2023.04.18.537378 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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