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

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.

AutoCoEv-A High-Throughput In Silico Pipeline for Predicting Inter-Protein Coevolution.

Protein-protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, an evolutionary analysis of large arrays of proteins in silico is a highly time-consuming effort that has limited the usage of this method for protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly, open source, computational pipeline for the search of coevolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting coevolution, AutoCoEv achieves a seamless automation and parallelization of the workflow. Importantly, we provide a patch to the CAPS2 source code to strengthen its statistical output, allowing for multiple comparison corrections and an enhanced analysis of the results. We apply the pipeline to inspect coevolution among 324 proteins identified to be located at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, can be used to predict functional interactions from large datasets in a time- and cost-efficient manner.

Comparative analysis of nutrient content and energy of eggs from different chicken genotypes.

BACKGROUND: Eggs are important foods in the daily diet of humans and have great biological activity and a high digestibility. Egg yolk is a good source of biologically active substances such as fatty acids, phospholipids, sterols and tocopherols. The eggs of seven chicken genotypes were analyzed for their chemical composition, and a detailed study of the lipids in egg yolk was conducted. RESULTS: Energy composition of the egg yolk and egg albumen was 29.06-30.51 MJ kg-1 and 19.77-20.93 MJ kg-1 respectively. Regarding their chemical composition: water ranged from 471.7 to 515.4 g kg-1 and 878.3-885.9 g kg-1 ; fat content in dry matter ranged from 607 to 647 g kg-1 and 6.7-11.6 g kg-1 ; protein varied from 302 to 331.7 g kg-1 and 823.6-892.5 g kg-1 ; ash ranged from 33.7 to 37.7 g kg-1 and 63.8-74.0 g kg-1 ; and nitrogen-free extracts ranged from 12.7 to 36.5 g kg-1 and 35.0-96.2 g kg-1 . The sterols and phospholipids in the yolk lipids were 16-26 g kg-1 and 59-127 g kg-1 . The main fatty acids in the lipids were oleic (39.1-47.3%) and palmitic (26.0-35.5%) acids. Cholesterol in the yolk lipids ranged from 15.9 to 25.9 g kg-1 . Phosphatidylcholine (389-573 g kg-1 ), phosphatidylethanolamine (219-355 g kg-1 ) and phosphatidylinositol (112-284 g kg-1 ) were the main phospholipids. The content of saturated fatty acids in the phospholipids was significantly higher than that in triacylglycerols. CONCLUSION: Small variations in the chemical composition of eggs from seven different genotypes were observed. Significant differences in the fatty acid compositions of the main classes of phospholipids and the triacylglycerol fraction were established. © 2019 Society of Chemical Industry.

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

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.