Improving gene regulatory network inference by incorporating rates of transcriptional change
Organisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, C...
| Main Authors: | , , , , |
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| Format: | Preprint |
| Language: | Inglés |
| Published: |
Cold Spring Harbor Laboratory
2016
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| Online Access: | https://hdl.handle.net/10568/165166 |
| _version_ | 1855517046747430912 |
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| author | Desai, Jigar S. Sartor, Ryan C. Lawas, Lovely Mae Jagadish, Krishna S.V. Doherty, Colleen J. |
| author_browse | Desai, Jigar S. Doherty, Colleen J. Jagadish, Krishna S.V. Lawas, Lovely Mae Sartor, Ryan C. |
| author_facet | Desai, Jigar S. Sartor, Ryan C. Lawas, Lovely Mae Jagadish, Krishna S.V. Doherty, Colleen J. |
| author_sort | Desai, Jigar S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Organisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, ChIP-Seq and similar validation methods are challenging to employ in non-model species. Improving the accuracy of computational inference methods can significantly reduce the cost and time of subsequent validation experiments. We have developed ExRANGES, an approach that improves the ability to computationally infer TRN from time series expression data. ExRANGES utilizes both the rate of change in expression and the absolute expression level to identify TRN connections. We evaluated ExRANGES in five data sets from different model systems. ExRANGES improved the identification of experimentally validated transcription factor targets for all species tested, even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships enhances the utility of network inference approaches in non-model species where experimental validation is challenging. We integrated ExRANGES with two different network construction approaches and it has been implemented as an R package available here:http://github.com/DohertyLab/ExRANGES.To install the package type:devtools |
| format | Preprint |
| id | CGSpace165166 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Cold Spring Harbor Laboratory |
| publisherStr | Cold Spring Harbor Laboratory |
| record_format | dspace |
| spelling | CGSpace1651662025-05-14T10:23:54Z Improving gene regulatory network inference by incorporating rates of transcriptional change Desai, Jigar S. Sartor, Ryan C. Lawas, Lovely Mae Jagadish, Krishna S.V. Doherty, Colleen J. Organisms respond to changes in their environment through transcriptional regulatory networks (TRNs). The regulatory hierarchy of these networks can be inferred from expression data. Computational approaches to identify TRNs can be applied in any species where quality RNA can be acquired, However, ChIP-Seq and similar validation methods are challenging to employ in non-model species. Improving the accuracy of computational inference methods can significantly reduce the cost and time of subsequent validation experiments. We have developed ExRANGES, an approach that improves the ability to computationally infer TRN from time series expression data. ExRANGES utilizes both the rate of change in expression and the absolute expression level to identify TRN connections. We evaluated ExRANGES in five data sets from different model systems. ExRANGES improved the identification of experimentally validated transcription factor targets for all species tested, even in unevenly spaced and sparse data sets. This improved ability to predict known regulator-target relationships enhances the utility of network inference approaches in non-model species where experimental validation is challenging. We integrated ExRANGES with two different network construction approaches and it has been implemented as an R package available here:http://github.com/DohertyLab/ExRANGES.To install the package type:devtools 2016-12-24 2024-12-19T12:54:46Z 2024-12-19T12:54:46Z Preprint https://hdl.handle.net/10568/165166 en Open Access Cold Spring Harbor Laboratory Desai, Jigar S.; Sartor, Ryan C.; Lawas, Lovely Mae; Jagadish, SV Krishna and Doherty, Colleen J. 2016. Improving gene regulatory network inference by incorporating rates of transcriptional change. bioRxiv, 24 pages, ill. Ref. |
| spellingShingle | Desai, Jigar S. Sartor, Ryan C. Lawas, Lovely Mae Jagadish, Krishna S.V. Doherty, Colleen J. Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title | Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title_full | Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title_fullStr | Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title_full_unstemmed | Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title_short | Improving gene regulatory network inference by incorporating rates of transcriptional change |
| title_sort | improving gene regulatory network inference by incorporating rates of transcriptional change |
| url | https://hdl.handle.net/10568/165166 |
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