TF2Network

TF2Network predicts transcription factor regulators and reconstructs gene regulatory networks (GRNs) in Arabidopsis thaliana.

Key Features:

• Extensive PWM database: Uses 1,793 position weight matrices (PWMs) representing binding sites for 916 transcription factors.
• Integration of experimental datasets: Integrates TF binding site information with co-expression data and experimental Protein-DNA and Protein-Protein interaction datasets.
• Regulatory network reconstruction: Maps potential regulator–target relationships to delineate gene regulatory networks (GRNs).
• Predictive performance: Validated against experimental benchmarks, recovering correct regulators in 75–92% of test sets.
• Noise robustness and specificity: Demonstrates robustness to noise in input gene sets and reports a low false discovery rate compared to other plant-specific methods.
• Functional and regulatory annotation: Applied for systematic functional and regulatory gene annotations, including identification of novel TFs involved in circadian rhythm and stress response.

Scientific Applications:

• Regulatory network inference: Predicts TF regulators and constructs GRNs for sets of co-expressed or functionally related genes in Arabidopsis thaliana.
• Functional annotation: Enables systematic functional and regulatory annotation of genes using integrated binding and interaction evidence.
• Discovery of TFs in biological processes: Identifies candidate transcription factors involved in processes such as circadian rhythm and stress response.

Methodology:

Integrates TF binding site information (PWMs) with co-expression data and experimental Protein-DNA and Protein-Protein interaction datasets to predict regulators and assemble GRNs.