tuxnet: a simple interface to process RNA sequencing data and infer gene regulatory networks

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Predicting gene regulatory networks (GRNs) is crucial for understanding biological regulation.
• Existing methods often lack RNA-sequencing data integration, automation, or user-friendliness for non-bioinformaticians.

Purpose of the Study:

• To develop tuxnet, a user-friendly platform for processing raw RNA-sequencing data and inferring GRNs.
• To address limitations in current computational approaches for GRN prediction.

Main Methods:

• Tuxnet utilizes a modified tuxedo pipeline (HISAT2 + Cufflinks) for RNA-sequencing data processing.
• GRN inference is performed using either the genist (dynamic Bayesian network) or rtp-star (regression tree-based) algorithms.
• The platform features a graphical user interface for accessibility.

Main Results:

• Tuxnet successfully processed RNA-sequencing data and inferred GRNs from various datasets, including time-course, wild-type/mutant, developmental, and cell-type specific expression profiles.
• Case studies demonstrated tuxnet's versatility in analyzing different data types and its ability to generate testable hypotheses.
• The platform facilitated insights into gene regulations, such as those downstream of the Arabidopsis root stem cell regulator PAN.

Conclusions:

• Tuxnet provides an accessible pipeline for non-bioinformaticians to analyze transcriptome data and predict gene regulatory networks.
• The platform enables the assessment of network topology and identification of key regulators.
• Tuxnet enhances the prediction of causal regulations from gene expression data.