TFInfer

TFInfer infers genome-wide transcription factor activities from gene expression data to analyze regulatory dynamics across time points and experimental conditions.

Key Features:

• Genome-wide inference: Infers transcription factor activities at genome scale from gene expression datasets, including microarray data.
• Versatile data modeling: Models both time-series experiments and multiple independent conditions to accommodate diverse experimental designs.
• Performance optimization: Optimized for efficient processing of large genomic datasets.
• Built-in transcription factor database: Includes a TF database covering model organisms such as Saccharomyces cerevisiae and Escherichia coli.

Scientific Applications:

• Transcription factor identification: Identifying active transcription factors from microarray or other gene expression data.
• Dynamics of regulation: Studying dynamic changes in transcription factor activities over time or across experimental conditions.
• Regulatory network analysis in model organisms: Characterizing regulatory networks in yeast (Saccharomyces cerevisiae) and Escherichia coli as model systems.

Methodology:

TFInfer infers transcription factor activities from gene expression (e.g., microarray) using a built-in TF database (including Saccharomyces cerevisiae and Escherichia coli) and supports modeling of time-series and multiple independent conditions with computational optimizations for large datasets.