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Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

BACKGROUND: A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation...

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Autores principales: Hawkins, Troy, Chitale, Meghana, Kihara, Daisuke
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882935/
https://www.ncbi.nlm.nih.gov/pubmed/20482861
http://dx.doi.org/10.1186/1471-2105-11-265
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author Hawkins, Troy
Chitale, Meghana
Kihara, Daisuke
author_facet Hawkins, Troy
Chitale, Meghana
Kihara, Daisuke
author_sort Hawkins, Troy
collection PubMed
description BACKGROUND: A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. RESULTS: Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. CONCLUSION: The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.
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spelling pubmed-28829352010-06-10 Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP Hawkins, Troy Chitale, Meghana Kihara, Daisuke BMC Bioinformatics Research article BACKGROUND: A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. RESULTS: Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. CONCLUSION: The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks. BioMed Central 2010-05-19 /pmc/articles/PMC2882935/ /pubmed/20482861 http://dx.doi.org/10.1186/1471-2105-11-265 Text en Copyright ©2010 Hawkins 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
Hawkins, Troy
Chitale, Meghana
Kihara, Daisuke
Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title_full Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title_fullStr Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title_full_unstemmed Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title_short Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP
title_sort functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by pfp
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882935/
https://www.ncbi.nlm.nih.gov/pubmed/20482861
http://dx.doi.org/10.1186/1471-2105-11-265
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