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Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data

Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is th...

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Autores principales: Simeone, Angela, Marsico, Giovanni, Collinet, Claudio, Galvez, Thierry, Kalaidzidis, Yannis, Zerial, Marino, Beyer, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154648/
https://www.ncbi.nlm.nih.gov/pubmed/25188415
http://dx.doi.org/10.1371/journal.pcbi.1003801
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author Simeone, Angela
Marsico, Giovanni
Collinet, Claudio
Galvez, Thierry
Kalaidzidis, Yannis
Zerial, Marino
Beyer, Andreas
author_facet Simeone, Angela
Marsico, Giovanni
Collinet, Claudio
Galvez, Thierry
Kalaidzidis, Yannis
Zerial, Marino
Beyer, Andreas
author_sort Simeone, Angela
collection PubMed
description Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.
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spelling pubmed-41546482014-09-08 Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data Simeone, Angela Marsico, Giovanni Collinet, Claudio Galvez, Thierry Kalaidzidis, Yannis Zerial, Marino Beyer, Andreas PLoS Comput Biol Research Article Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens. Public Library of Science 2014-09-04 /pmc/articles/PMC4154648/ /pubmed/25188415 http://dx.doi.org/10.1371/journal.pcbi.1003801 Text en © 2014 Simeone et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Simeone, Angela
Marsico, Giovanni
Collinet, Claudio
Galvez, Thierry
Kalaidzidis, Yannis
Zerial, Marino
Beyer, Andreas
Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title_full Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title_fullStr Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title_full_unstemmed Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title_short Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data
title_sort revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4154648/
https://www.ncbi.nlm.nih.gov/pubmed/25188415
http://dx.doi.org/10.1371/journal.pcbi.1003801
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