BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus
Abstract Background Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to...
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| Format: | Article |
| Language: | English |
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BMC
2019-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-3042-8 |
| _version_ | 1818922685948755968 |
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| author | Patrick M. Staunton Aleksandra A. Miranda-CasoLuengo Brendan J. Loftus Isobel Claire Gormley |
| author_facet | Patrick M. Staunton Aleksandra A. Miranda-CasoLuengo Brendan J. Loftus Isobel Claire Gormley |
| author_sort | Patrick M. Staunton |
| collection | DOAJ |
| description | Abstract Background Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining ‘primary’ and ‘auxiliary’ data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. Results We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. Conclusions The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms. |
| first_indexed | 2024-12-20T01:57:29Z |
| format | Article |
| id | doaj.art-1dffbbe7f486421f869fbccd6bd9728b |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-20T01:57:29Z |
| publishDate | 2019-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-1dffbbe7f486421f869fbccd6bd9728b2022-12-21T19:57:27ZengBMCBMC Bioinformatics1471-21052019-09-0120112110.1186/s12859-019-3042-8BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessusPatrick M. Staunton0Aleksandra A. Miranda-CasoLuengo1Brendan J. Loftus2Isobel Claire Gormley3School of Medicine, Conway Institute, University College DublinMoyne Institute of Preventive Medicine, Department of Microbiology, Trnity College DublinSchool of Medicine, Conway Institute, University College DublinSchool of Mathematics and Statistics, Insight Centre for Data Analytics, University College DublinAbstract Background Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining ‘primary’ and ‘auxiliary’ data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. Results We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. Conclusions The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.http://link.springer.com/article/10.1186/s12859-019-3042-8Gene regulatory networkMycobacterium abscessusBayesian inferenceData integration |
| spellingShingle | Patrick M. Staunton Aleksandra A. Miranda-CasoLuengo Brendan J. Loftus Isobel Claire Gormley BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus BMC Bioinformatics Gene regulatory network Mycobacterium abscessus Bayesian inference Data integration |
| title | BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus |
| title_full | BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus |
| title_fullStr | BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus |
| title_full_unstemmed | BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus |
| title_short | BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus |
| title_sort | binder computationally inferring a gene regulatory network for mycobacterium abscessus |
| topic | Gene regulatory network Mycobacterium abscessus Bayesian inference Data integration |
| url | http://link.springer.com/article/10.1186/s12859-019-3042-8 |
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