Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen
Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Cold Spring Harbor Laboratory Press
2013
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| Online Access: | http://hdl.handle.net/1721.1/76795 https://orcid.org/0000-0002-2724-7228 |
| _version_ | 1811096984350621696 |
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| author | Nir, Oaz Bakal, Chris Perrimon, Norbert Berger, Bonnie |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Nir, Oaz Bakal, Chris Perrimon, Norbert Berger, Bonnie |
| author_sort | Nir, Oaz |
| collection | MIT |
| description | Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations. |
| first_indexed | 2024-09-23T16:52:30Z |
| format | Article |
| id | mit-1721.1/76795 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:52:30Z |
| publishDate | 2013 |
| publisher | Cold Spring Harbor Laboratory Press |
| record_format | dspace |
| spelling | mit-1721.1/767952022-10-03T08:50:13Z Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen Nir, Oaz Bakal, Chris Perrimon, Norbert Berger, Bonnie Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mathematics Nir, Oaz Bakal, Chris Berger, Bonnie Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations. United States. Dept. of Energy (Computational Science Graduate Fellowship) Leukemia & Lymphoma Society of America Wellcome Trust (London, England) (Research Career Development Fellow) National Institutes of Health (U.S.) (Grant 1R01GM081871-01A1) 2013-02-13T16:05:33Z 2013-02-13T16:05:33Z 2010-02 Article http://purl.org/eprint/type/JournalArticle 1088-9051 http://hdl.handle.net/1721.1/76795 Nir, O. et al. “Inference of RhoGAP/GTPase Regulation Using Single-cell Morphological Data from a Combinatorial RNAi Screen.” Genome Research 20.3 (2010): 372–380. Copyright © 2010 by Cold Spring Harbor Laboratory Press https://orcid.org/0000-0002-2724-7228 en_US http://dx.doi.org/10.1101/gr.100248.109 Genome Research Creative Commons Attribution Non-Commercial http://creativecommons.org/licenses/by-nc/3.0 application/pdf Cold Spring Harbor Laboratory Press Genome Research |
| spellingShingle | Nir, Oaz Bakal, Chris Perrimon, Norbert Berger, Bonnie Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title | Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title_full | Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title_fullStr | Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title_full_unstemmed | Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title_short | Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen |
| title_sort | inference of rhogap gtpase regulation using single cell morphological data from a combinatorial rnai screen |
| url | http://hdl.handle.net/1721.1/76795 https://orcid.org/0000-0002-2724-7228 |
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