tuxnet

tuxnet infers gene regulatory networks from raw RNA sequencing (RNA-seq) data and associated temporal and spatial expression profiles to identify regulatory relationships and network topology.

Key Features:

• Modified Tuxedo pipeline integration: Integrates a modified Tuxedo pipeline using HISAT2 and Cufflinks for RNA-seq read alignment and transcript assembly against a reference genome.
• Gene regulatory network inference: Implements Dynamic Bayesian Network (Genist) inference for temporal data and a regression tree–based pipeline (RTP-Star) for network construction.
• Temporal and spatial data support: Handles time-course (temporal) and spatial expression profiles for network inference.
• Organism-agnostic processing: Processes raw RNA-seq data from any organism with an available reference genome.
• Predictive outputs: Predicts causal regulatory relationships, assesses network topology, and identifies key regulatory genes from expression data.

Scientific Applications:

• Regulatory candidate identification: Predicts gene regulatory networks to identify important biological regulators and causal regulatory interactions from expression profiles.
• PAN inducible line analysis (Arabidopsis): Infers regulations downstream of PERIANTHIA (PAN) from time-course expression data in an inducible Arabidopsis line.
• ATHB13 network inference: Constructs regulatory networks for ATHB13 using wild-type and mutant expression profiles.
• Developmental and cell-type studies: Analyzes developmental leaf data and root cell-type spatial expression to generate testable hypotheses about regulatory interactions.

Methodology:

Uses a modified Tuxedo pipeline incorporating HISAT2 and Cufflinks for alignment and assembly, and applies Dynamic Bayesian Network inference (Genist) and a regression tree–based pipeline (RTP-Star) for gene regulatory network inference.