Augusta: A FAIRified Python package for RNA-Seq-Based Inference of Gene Regulatory and Boolean Networks

Augusta: A FAIRified Python package for RNA-Seq-Based Inference of Gene Regulatory and Boolean Networks

Abstrakt

Understanding gene regulation is essential for unveiling complex mechanisms that govern biological processes. RNA sequencing (RNA-Seq) has emerged as a powerful technique for quantifying gene expression across diverse conditions genome-wide. Computational models, such as Gene Regulatory Networks (GRNs) and Boolean Networks (BNs), offer unique insights into gene interactions, elucidating key regulatory processes within the studied organism. Augusta is a Python package designed to infer computational models from time-series RNA-Seq data, enabling efficient derivation of GRNs and BNs on a genome-wide scale. The package follows a multi-step pipeline, beginning with a count table normalization, followed by GRN inference using mutual information calculations, and finishing with a two-step validation approach. This validation includes de novo discovery of transcription factor (TF) motifs, after which curated databases are queried to enhance and verify the inferred network. Additionally, Augusta facilitates BN inference by integrating curated data sources with logical rules, enabling a more comprehensive network analysis. Ongoing development aims to ensure that Augusta aligns with the principles of Findability, Accessibility, Interoperability, and Reusability for Research Software (FAIR-RS). Supported by the FAIR-IMPACT grant under the Assessment and improvement of Research Software support action, the Python package promises to enhance reproducibility and transparency in GRN and BN inference. Augusta is available from github.com/JanaMus/Augusta along with documentation, examples, and tutorials.

Understanding gene regulation is essential for unveiling complex mechanisms that govern biological processes. RNA sequencing (RNA-Seq) has emerged as a powerful technique for quantifying gene expression across diverse conditions genome-wide. Computational models, such as Gene Regulatory Networks (GRNs) and Boolean Networks (BNs), offer unique insights into gene interactions, elucidating key regulatory processes within the studied organism. Augusta is a Python package designed to infer computational models from time-series RNA-Seq data, enabling efficient derivation of GRNs and BNs on a genome-wide scale. The package follows a multi-step pipeline, beginning with a count table normalization, followed by GRN inference using mutual information calculations, and finishing with a two-step validation approach. This validation includes de novo discovery of transcription factor (TF) motifs, after which curated databases are queried to enhance and verify the inferred network. Additionally, Augusta facilitates BN inference by integrating curated data sources with logical rules, enabling a more comprehensive network analysis. Ongoing development aims to ensure that Augusta aligns with the principles of Findability, Accessibility, Interoperability, and Reusability for Research Software (FAIR-RS). Supported by the FAIR-IMPACT grant under the Assessment and improvement of Research Software support action, the Python package promises to enhance reproducibility and transparency in GRN and BN inference. Augusta is available from github.com/JanaMus/Augusta along with documentation, examples, and tutorials.

MUSILOVÁ, J.; VAFEK, Z.; ZIMMER, R.; HELIKAR, T.; SEDLÁŘ, K.