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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4154648?pdf=render |
| _version_ | 1811213003410898944 |
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| author | Angela Simeone Giovanni Marsico Claudio Collinet Thierry Galvez Yannis Kalaidzidis Marino Zerial Andreas Beyer |
| author_facet | Angela Simeone Giovanni Marsico Claudio Collinet Thierry Galvez Yannis Kalaidzidis Marino Zerial Andreas Beyer |
| author_sort | Angela Simeone |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T05:38:50Z |
| format | Article |
| id | doaj.art-78fbf104473246ef95cd5584349bfdb4 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T05:38:50Z |
| publishDate | 2014-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-78fbf104473246ef95cd5584349bfdb42022-12-22T03:45:43ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582014-09-01109e100380110.1371/journal.pcbi.1003801Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data.Angela SimeoneGiovanni MarsicoClaudio CollinetThierry GalvezYannis KalaidzidisMarino ZerialAndreas BeyerFunctional 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.http://europepmc.org/articles/PMC4154648?pdf=render |
| spellingShingle | Angela Simeone Giovanni Marsico Claudio Collinet Thierry Galvez Yannis Kalaidzidis Marino Zerial Andreas Beyer Revealing molecular mechanisms by integrating high-dimensional functional screens with protein interaction data. PLoS Computational Biology |
| 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 |
| url | http://europepmc.org/articles/PMC4154648?pdf=render |
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