BMC Genomics | |
PNAC: a protein nucleolar association classifier | |
Research Article | |
Geoffrey J Barton1  Michelle S Scott1  Angus I Lamond2  François-Michel Boisvert2  | |
[1] Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dow Street, DD1 5EH, Dundee, UK;Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street, DD1 5EH, Dundee, UK; | |
关键词: Gene Ontology; Positive Predictive Value; Reliability Index; Nucleolar Protein; Biological Process Term; | |
DOI : 10.1186/1471-2164-12-74 | |
received in 2010-09-30, accepted in 2011-01-27, 发布年份 2011 | |
来源: Springer | |
【 摘 要 】
BackgroundAlthough 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.ResultsTo 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.ConclusionsOur 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.
【 授权许可】
CC BY
© Scott et al; licensee BioMed Central Ltd. 2011
【 预 览 】
Files | Size | Format | View |
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RO202311092484809ZK.pdf | 722KB | download |
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