BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

DC FieldValueLanguage
dc.contributor.authorStaunton, Patrick M.-
dc.contributor.authorMiranda-CasoLuengo, Aleksandra A.-
dc.contributor.authorLoftus, Brendan J.-
dc.contributor.authorGormley, Isobel Claire-
dc.date.accessioned2019-11-01T15:25:28Z-
dc.date.available2019-11-01T15:25:28Z-
dc.date.copyright2019 the Authorsen_US
dc.date.issued2019-09-10-
dc.identifier.citationBMC Bioinformaticsen_US
dc.identifier.urihttp://hdl.handle.net/10197/11187-
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.description.sponsorshipWellcome Trusten_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.subjectGene regulatory networken_US
dc.subjectMycobacterium abscessusen_US
dc.subjectBayesian inferenceen_US
dc.subjectData integrationen_US
dc.titleBINDER: computationally inferring a gene regulatory network for Mycobacterium abscessusen_US
dc.typeJournal Articleen_US
dc.statusPeer revieweden_US
dc.identifier.volume20en_US
dc.citation.otherArticle Number: 446en_US
dc.identifier.doi10.1186/s12859-019-3042-8-
dc.neeo.contributorStaunton|Patrick M.|aut|-
dc.neeo.contributorMiranda-CasoLuengo|Aleksandra A.|aut|-
dc.neeo.contributorLoftus|Brendan J.|aut|-
dc.neeo.contributorGormley|Isobel Claire|aut|-
dc.description.othersponsorshipInsight Research Centreen_US
dc.date.updated2019-09-16T12:41:16Z-
dc.identifier.grantid099817/Z/12/Z-
dc.identifier.grantid12/RC/2289_P2-
item.fulltextWith Fulltext-
item.grantfulltextopen-
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Medicine Research Collection
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This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.