University of Oulu

Petar B. Petrov, James M. Considine, Valerio Izzi, Alexandra Naba; Matrisome AnalyzeR – a suite of tools to annotate and quantify ECM molecules in big datasets across organisms. J Cell Sci 1 September 2023; 136 (17): jcs261255. doi:

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

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Author: Petrov, Petar B.1; Considine, James M.2; Izzi, Valerio3,4;
Organizations: 1Infotech Institute, University of Oulu , FI-90014 Oulu , Finland
2Department of Physiology and Biophysics , University of Illinois Chicago , Chicago, IL 60612 , USA
3Faculty of Biochemistry and Molecular Medicine & Faculty of Medicine, BioIM Unit , University of Oulu , FI-90014 Oulu , Finland
4Foundation for the Finnish Cancer Institute , Tukholmankatu 8, Fl-00290 Helsinki , Finland
5University of Illinois Cancer Center , Chicago, IL 60612 , USA
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
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Language: English
Published: Company of Biologists, 2023
Publish Date: 2023-11-08


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.

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Series: Journal of cell science
ISSN: 0021-9533
ISSN-E: 1477-9137
ISSN-L: 0021-9533
Volume: 136
Issue: 17
DOI: 10.1242/jcs.261255
Type of Publication: A1 Journal article – refereed
Field of Science: 1182 Biochemistry, cell and molecular biology
Funding: This work was supported in part by the National Institutes of Health (U01HG012680, R21CA261642 and R01CA232517 to A.N.) and by a start-up fund from the Department of Physiology and Biophysics of the University of Illinois Chicago (A.N.). This research is connected to the DigiHealth-project, a strategic profiling project at the University of Oulu (V.I.) and the Infotech Institute (V.I., P.B.P.). The project is supported by the Academy of Finland (DECISION 326291 to V.I.), the Cancer Foundation Finland (V.I.), the Finnish Cancer Institute, and K. Albin Johansson Cancer Research Fellowship fund (V.I.). Open Access funding provided by National Institutes of Health. Deposited in PMC for immediate release.
Copyright information: © 2023. Published by The Company of Biologists Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.