Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana
Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co‐function netw...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/90274 http://hdl.handle.net/10220/48497 |
| _version_ | 1826129134780153856 |
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| author | Hansen, Bjoern Oest Meyer, Etienne H. Ferrari, Camilla Vaid, Neha Movahedi, Sara Vandepoele, Klaas Nikoloski, Zoran Mutwil, Marek |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences Hansen, Bjoern Oest Meyer, Etienne H. Ferrari, Camilla Vaid, Neha Movahedi, Sara Vandepoele, Klaas Nikoloski, Zoran Mutwil, Marek |
| author_sort | Hansen, Bjoern Oest |
| collection | NTU |
| description | Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co‐function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user‐friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting‐edge community tool to guide experimentalists. |
| first_indexed | 2024-10-01T07:35:45Z |
| format | Journal Article |
| id | ntu-10356/90274 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:35:45Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/902742023-02-28T17:03:12Z Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana Hansen, Bjoern Oest Meyer, Etienne H. Ferrari, Camilla Vaid, Neha Movahedi, Sara Vandepoele, Klaas Nikoloski, Zoran Mutwil, Marek School of Biological Sciences Gene Function Prediction DRNTU::Science::Biological sciences Ensemble Prediction Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co‐function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user‐friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting‐edge community tool to guide experimentalists. Accepted version 2019-05-30T08:36:10Z 2019-12-06T17:44:33Z 2019-05-30T08:36:10Z 2019-12-06T17:44:33Z 2018 Journal Article Hansen, B. O., Meyer, E. H., Ferrari, C., Vaid, N., Movahedi, S., Vandepoele, K., . . . Mutwil, M. (2018). Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana. New Phytologist, 217(4), 1521-1534. doi:10.1111/nph.14921 0028-646X https://hdl.handle.net/10356/90274 http://hdl.handle.net/10220/48497 10.1111/nph.14921 en New Phytologist © 2017 New Phytologist Trust. All rights reserved. This paper was published by Wiley in New Phytologist and is made available with permission of New Phytologist Trust. The definitive version is available at www.newphytologist.com via https://doi.org/10.1111/nph.14921 31 p. application/pdf |
| spellingShingle | Gene Function Prediction DRNTU::Science::Biological sciences Ensemble Prediction Hansen, Bjoern Oest Meyer, Etienne H. Ferrari, Camilla Vaid, Neha Movahedi, Sara Vandepoele, Klaas Nikoloski, Zoran Mutwil, Marek Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana |
| title | Ensemble gene function prediction database reveals genes important for complex I formation in
Arabidopsis thaliana |
| title_full | Ensemble gene function prediction database reveals genes important for complex I formation in
Arabidopsis thaliana |
| title_fullStr | Ensemble gene function prediction database reveals genes important for complex I formation in
Arabidopsis thaliana |
| title_full_unstemmed | Ensemble gene function prediction database reveals genes important for complex I formation in
Arabidopsis thaliana |
| title_short | Ensemble gene function prediction database reveals genes important for complex I formation in
Arabidopsis thaliana |
| title_sort | ensemble gene function prediction database reveals genes important for complex i formation in arabidopsis thaliana |
| topic | Gene Function Prediction DRNTU::Science::Biological sciences Ensemble Prediction |
| url | https://hdl.handle.net/10356/90274 http://hdl.handle.net/10220/48497 |
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