Augusta: From RNA-Seq to Gene Regulatory Networks and Boolean Models
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Date
2024-01-20
Authors
Musilová, Jana
Vafek, Zdeněk
Punyia, Bhanwar
Zimmer, Ralf
Helikar, Tomáš
Sedlář, Karel
0000-0001-6910-6047Advisor
Referee
Mark
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Elsevier
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Abstract
Computational 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.
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Citation
Computational and Structural Biotechnology Journal. 2024, vol. 23, issue December 2024, p. 783-790.
https://www.sciencedirect.com/science/article/pii/S2001037024000138?via%3Dihub
https://www.sciencedirect.com/science/article/pii/S2001037024000138?via%3Dihub
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Peer-reviewed
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en
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://creativecommons.org/licenses/by-nc-nd/4.0/