Augusta: From RNA-Seq to Gene Regulatory Networks and Boolean Models

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dc.contributor.authorMusilová, Janacs
dc.contributor.authorVafek, Zdeněkcs
dc.contributor.authorPunyia, Bhanwarcs
dc.contributor.authorZimmer, Ralfcs
dc.contributor.authorHelikar, Tomášcs
dc.contributor.authorSedlář, Karelcs
dc.coverage.issueDecember 2024cs
dc.coverage.volume23cs
dc.date.issued2024-01-20cs
dc.description.abstractComputational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.en
dc.formattextcs
dc.format.extent783-790cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputational and Structural Biotechnology Journal. 2024, vol. 23, issue December 2024, p. 783-790.en
dc.identifier.doi10.1016/j.csbj.2024.01.013cs
dc.identifier.issn2001-0370cs
dc.identifier.orcid0000-0001-6910-6047cs
dc.identifier.orcid0000-0002-8269-4020cs
dc.identifier.other187210cs
dc.identifier.researcheridAHB-1545-2022cs
dc.identifier.researcheridK-1120-2014cs
dc.identifier.scopus56309904900cs
dc.identifier.urihttp://hdl.handle.net/11012/245285
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofComputational and Structural Biotechnology Journalcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2001037024000138?via%3Dihubcs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2001-0370/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectPython packageen
dc.subjectgene interactionsen
dc.subjectmutual informationen
dc.subjecttranscription factoren
dc.subjectbinding motifsen
dc.subjectdatabasesen
dc.titleAugusta: From RNA-Seq to Gene Regulatory Networks and Boolean Modelsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-187210en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:59en
sync.item.modts2025.01.17 16:42:06en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
thesis.grantorVysoké učení technické v Brně. Ústav soudního inženýrství. Ústav soudního inženýrstvícs
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