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PNAC: a protein nucleolar association classifier
BACKGROUND: Although primarily known as the site of ribosome subunit production, the nucleolus is involved in numerous and diverse cellular processes. Recent large-scale proteomics projects have identified thousands of human proteins that associate with the nucleolus. However, in most cases, we know...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3038921/ https://www.ncbi.nlm.nih.gov/pubmed/21272300 http://dx.doi.org/10.1186/1471-2164-12-74 |
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author | Scott, Michelle S Boisvert, François-Michel Lamond, Angus I Barton, Geoffrey J |
author_facet | Scott, Michelle S Boisvert, François-Michel Lamond, Angus I Barton, Geoffrey J |
author_sort | Scott, Michelle S |
collection | PubMed |
description | BACKGROUND: Although primarily known as the site of ribosome subunit production, the nucleolus is involved in numerous and diverse cellular processes. Recent large-scale proteomics projects have identified thousands of human proteins that associate with the nucleolus. However, in most cases, we know neither the fraction of each protein pool that is nucleolus-associated nor whether their association is permanent or conditional. RESULTS: To describe the dynamic localisation of proteins in the nucleolus, we investigated the extent of nucleolar association of proteins by first collating an extensively curated literature-derived dataset. This dataset then served to train a probabilistic predictor which integrates gene and protein characteristics. Unlike most previous experimental and computational studies of the nucleolar proteome that produce large static lists of nucleolar proteins regardless of their extent of nucleolar association, our predictor models the fluidity of the nucleolus by considering different classes of nucleolar-associated proteins. The new method predicts all human proteins as either nucleolar-enriched, nucleolar-nucleoplasmic, nucleolar-cytoplasmic or non-nucleolar. Leave-one-out cross validation tests reveal sensitivity values for these four classes ranging from 0.72 to 0.90 and positive predictive values ranging from 0.63 to 0.94. The overall accuracy of the classifier was measured to be 0.85 on an independent literature-based test set and 0.74 using a large independent quantitative proteomics dataset. While the three nucleolar-association groups display vastly different Gene Ontology biological process signatures and evolutionary characteristics, they collectively represent the most well characterised nucleolar functions. CONCLUSIONS: Our proteome-wide classification of nucleolar association provides a novel representation of the dynamic content of the nucleolus. This model of nucleolar localisation thus increases the coverage while providing accurate and specific annotations of the nucleolar proteome. It will be instrumental in better understanding the central role of the nucleolus in the cell and its interaction with other subcellular compartments. |
format | Text |
id | pubmed-3038921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30389212011-02-28 PNAC: a protein nucleolar association classifier Scott, Michelle S Boisvert, François-Michel Lamond, Angus I Barton, Geoffrey J BMC Genomics Research Article BACKGROUND: Although primarily known as the site of ribosome subunit production, the nucleolus is involved in numerous and diverse cellular processes. Recent large-scale proteomics projects have identified thousands of human proteins that associate with the nucleolus. However, in most cases, we know neither the fraction of each protein pool that is nucleolus-associated nor whether their association is permanent or conditional. RESULTS: To describe the dynamic localisation of proteins in the nucleolus, we investigated the extent of nucleolar association of proteins by first collating an extensively curated literature-derived dataset. This dataset then served to train a probabilistic predictor which integrates gene and protein characteristics. Unlike most previous experimental and computational studies of the nucleolar proteome that produce large static lists of nucleolar proteins regardless of their extent of nucleolar association, our predictor models the fluidity of the nucleolus by considering different classes of nucleolar-associated proteins. The new method predicts all human proteins as either nucleolar-enriched, nucleolar-nucleoplasmic, nucleolar-cytoplasmic or non-nucleolar. Leave-one-out cross validation tests reveal sensitivity values for these four classes ranging from 0.72 to 0.90 and positive predictive values ranging from 0.63 to 0.94. The overall accuracy of the classifier was measured to be 0.85 on an independent literature-based test set and 0.74 using a large independent quantitative proteomics dataset. While the three nucleolar-association groups display vastly different Gene Ontology biological process signatures and evolutionary characteristics, they collectively represent the most well characterised nucleolar functions. CONCLUSIONS: Our proteome-wide classification of nucleolar association provides a novel representation of the dynamic content of the nucleolus. This model of nucleolar localisation thus increases the coverage while providing accurate and specific annotations of the nucleolar proteome. It will be instrumental in better understanding the central role of the nucleolus in the cell and its interaction with other subcellular compartments. BioMed Central 2011-01-27 /pmc/articles/PMC3038921/ /pubmed/21272300 http://dx.doi.org/10.1186/1471-2164-12-74 Text en Copyright ©2011 Scott et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Scott, Michelle S Boisvert, François-Michel Lamond, Angus I Barton, Geoffrey J PNAC: a protein nucleolar association classifier |
title | PNAC: a protein nucleolar association classifier |
title_full | PNAC: a protein nucleolar association classifier |
title_fullStr | PNAC: a protein nucleolar association classifier |
title_full_unstemmed | PNAC: a protein nucleolar association classifier |
title_short | PNAC: a protein nucleolar association classifier |
title_sort | pnac: a protein nucleolar association classifier |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3038921/ https://www.ncbi.nlm.nih.gov/pubmed/21272300 http://dx.doi.org/10.1186/1471-2164-12-74 |
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