Coverage and error models of protein-protein interaction data by directed graph analysis

Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or fa...

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Detalles Bibliográficos
Autores principales: Chiang, Tony, Scholtens, Denise, Sarkar, Deepayan, Gentleman, Robert, Huber, Wolfgang
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375024/
https://www.ncbi.nlm.nih.gov/pubmed/17845715
http://dx.doi.org/10.1186/gb-2007-8-9-r186
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author Chiang, Tony
Scholtens, Denise
Sarkar, Deepayan
Gentleman, Robert
Huber, Wolfgang
author_facet Chiang, Tony
Scholtens, Denise
Sarkar, Deepayan
Gentleman, Robert
Huber, Wolfgang
author_sort Chiang, Tony
collection PubMed
description Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or false-negative observations, and estimates of the stochastic error rates that affect the data. These traits provide a prerequisite for the estimation of the protein interactome and its modules.
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spelling pubmed-23750242008-05-12 Coverage and error models of protein-protein interaction data by directed graph analysis Chiang, Tony Scholtens, Denise Sarkar, Deepayan Gentleman, Robert Huber, Wolfgang Genome Biol Method Using a directed graph model for bait to prey systems and a multinomial error model, we assessed the error statistics in all published large-scale datasets for Saccharomyces cerevisiae and characterized them by three traits: the set of tested interactions, artifacts that lead to false-positive or false-negative observations, and estimates of the stochastic error rates that affect the data. These traits provide a prerequisite for the estimation of the protein interactome and its modules. BioMed Central 2007 2007-09-10 /pmc/articles/PMC2375024/ /pubmed/17845715 http://dx.doi.org/10.1186/gb-2007-8-9-r186 Text en Copyright © 2007 Chiang 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 Method
Chiang, Tony
Scholtens, Denise
Sarkar, Deepayan
Gentleman, Robert
Huber, Wolfgang
Coverage and error models of protein-protein interaction data by directed graph analysis
title Coverage and error models of protein-protein interaction data by directed graph analysis
title_full Coverage and error models of protein-protein interaction data by directed graph analysis
title_fullStr Coverage and error models of protein-protein interaction data by directed graph analysis
title_full_unstemmed Coverage and error models of protein-protein interaction data by directed graph analysis
title_short Coverage and error models of protein-protein interaction data by directed graph analysis
title_sort coverage and error models of protein-protein interaction data by directed graph analysis
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375024/
https://www.ncbi.nlm.nih.gov/pubmed/17845715
http://dx.doi.org/10.1186/gb-2007-8-9-r186
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AT sarkardeepayan coverageanderrormodelsofproteinproteininteractiondatabydirectedgraphanalysis
AT gentlemanrobert coverageanderrormodelsofproteinproteininteractiondatabydirectedgraphanalysis
AT huberwolfgang coverageanderrormodelsofproteinproteininteractiondatabydirectedgraphanalysis

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