Automated identification of pathways from quantitative genetic interaction data
Abstract High‐throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2010-06-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.1038/msb.2010.27 |
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| _version_ | 1849225774850113536 |
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| author | Alexis Battle Martin C Jonikas Peter Walter Jonathan S Weissman Daphne Koller |
| author_facet | Alexis Battle Martin C Jonikas Peter Walter Jonathan S Weissman Daphne Koller |
| author_sort | Alexis Battle |
| collection | DOAJ |
| description | Abstract High‐throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N‐linked glycosylation and ER‐associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail‐anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher‐level organisms. |
| format | Article |
| id | doaj-art-81bb991c9acc47febd55fed4ec539792 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2010-06-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-81bb991c9acc47febd55fed4ec5397922025-08-24T12:00:35ZengSpringer NatureMolecular Systems Biology1744-42922010-06-016111310.1038/msb.2010.27Automated identification of pathways from quantitative genetic interaction dataAlexis Battle0Martin C Jonikas1Peter Walter2Jonathan S Weissman3Daphne Koller4Department of Computer Science, Stanford UniversityDepartment of Cellular and Molecular Pharmacology, University of CaliforniaDepartment of Biochemistry and Biophysics, University of CaliforniaDepartment of Cellular and Molecular Pharmacology, University of CaliforniaDepartment of Computer Science, Stanford UniversityAbstract High‐throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N‐linked glycosylation and ER‐associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail‐anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher‐level organisms.https://doi.org/10.1038/msb.2010.27computational biologygenetic interactionpathway reconstructionprobabilistic methods |
| spellingShingle | Alexis Battle Martin C Jonikas Peter Walter Jonathan S Weissman Daphne Koller Automated identification of pathways from quantitative genetic interaction data Molecular Systems Biology computational biology genetic interaction pathway reconstruction probabilistic methods |
| title | Automated identification of pathways from quantitative genetic interaction data |
| title_full | Automated identification of pathways from quantitative genetic interaction data |
| title_fullStr | Automated identification of pathways from quantitative genetic interaction data |
| title_full_unstemmed | Automated identification of pathways from quantitative genetic interaction data |
| title_short | Automated identification of pathways from quantitative genetic interaction data |
| title_sort | automated identification of pathways from quantitative genetic interaction data |
| topic | computational biology genetic interaction pathway reconstruction probabilistic methods |
| url | https://doi.org/10.1038/msb.2010.27 |
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