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CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data

The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interact...

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Autores principales: Velasquez, Erick F., Garcia, Yenni A., Ramirez, Ivan, Gholkar, Ankur A., Torres, Jorge Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The American Society for Cell Biology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693966/
https://www.ncbi.nlm.nih.gov/pubmed/34432510
http://dx.doi.org/10.1091/mbc.E21-05-0257
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author Velasquez, Erick F.
Garcia, Yenni A.
Ramirez, Ivan
Gholkar, Ankur A.
Torres, Jorge Z.
author_facet Velasquez, Erick F.
Garcia, Yenni A.
Ramirez, Ivan
Gholkar, Ankur A.
Torres, Jorge Z.
author_sort Velasquez, Erick F.
collection PubMed
description The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.
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spelling pubmed-86939662022-01-31 CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data Velasquez, Erick F. Garcia, Yenni A. Ramirez, Ivan Gholkar, Ankur A. Torres, Jorge Z. Mol Biol Cell Brief Reports The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data. The American Society for Cell Biology 2021-11-01 /pmc/articles/PMC8693966/ /pubmed/34432510 http://dx.doi.org/10.1091/mbc.E21-05-0257 Text en © 2021 Velasquez et al. “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society for Cell Biology. https://creativecommons.org/licenses/by-nc-sa/3.0/This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License.
spellingShingle Brief Reports
Velasquez, Erick F.
Garcia, Yenni A.
Ramirez, Ivan
Gholkar, Ankur A.
Torres, Jorge Z.
CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title_full CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title_fullStr CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title_full_unstemmed CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title_short CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
title_sort canvs: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
topic Brief Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8693966/
https://www.ncbi.nlm.nih.gov/pubmed/34432510
http://dx.doi.org/10.1091/mbc.E21-05-0257
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