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PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data
[Image: see text] The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statist...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258137/ https://www.ncbi.nlm.nih.gov/pubmed/25177766 http://dx.doi.org/10.1021/pr500473n |
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author | Lavallée-Adam, Mathieu Rauniyar, Navin McClatchy, Daniel B. Yates, John R. |
author_facet | Lavallée-Adam, Mathieu Rauniyar, Navin McClatchy, Daniel B. Yates, John R. |
author_sort | Lavallée-Adam, Mathieu |
collection | PubMed |
description | [Image: see text] The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights. |
format | Online Article Text |
id | pubmed-4258137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-42581372015-09-01 PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data Lavallée-Adam, Mathieu Rauniyar, Navin McClatchy, Daniel B. Yates, John R. J Proteome Res [Image: see text] The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights. American Chemical Society 2014-09-01 2014-12-05 /pmc/articles/PMC4258137/ /pubmed/25177766 http://dx.doi.org/10.1021/pr500473n Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes. |
spellingShingle | Lavallée-Adam, Mathieu Rauniyar, Navin McClatchy, Daniel B. Yates, John R. PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data |
title | PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free
and Label-Based Protein Quantification Data |
title_full | PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free
and Label-Based Protein Quantification Data |
title_fullStr | PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free
and Label-Based Protein Quantification Data |
title_full_unstemmed | PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free
and Label-Based Protein Quantification Data |
title_short | PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free
and Label-Based Protein Quantification Data |
title_sort | psea-quant: a protein set enrichment analysis on label-free
and label-based protein quantification data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4258137/ https://www.ncbi.nlm.nih.gov/pubmed/25177766 http://dx.doi.org/10.1021/pr500473n |
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