iModulonDB

iModulonDB applies Independent Component Analysis (ICA) to bacterial transcriptomes to identify independently-modulated, co-regulated gene sets (iModulons) and infer their activities to elucidate microbial transcriptional regulation.

Key Features:

• Independent Component Analysis (ICA): Decomposes transcriptomic data into statistically independent components to reveal iModulons.
• iModulon definition: Identifies co-regulated, independently-modulated gene sets (iModulons) from bacterial transcriptomes.
• Activity inference: Infers iModulon activities across diverse environmental conditions from expression data.
• Regulator association: Associates iModulons with known genetic regulators and transcription factors.
• High-quality transcriptomic datasets: Operates on curated, high-quality bacterial transcriptomes to derive components and activities.
• Organism coverage: Contains data for Escherichia coli, Staphylococcus aureus, and Bacillus subtilis with a total of 204 iModulons.
• Curated gene membership: Provides curated gene lists and per-iModulon annotations linking genes to components.

Scientific Applications:

• Regulator-gene relationship analysis: Enables mapping of co-expressed gene sets to candidate regulators and transcription factors.
• Environmental response profiling: Characterizes how iModulon activities change across environmental conditions to study transcriptomic adaptation.
• Transcription factor discovery: Supports identification of previously unannotated regulators through component–regulator associations.
• Network-level interpretation: Facilitates interpretation of bacterial transcriptional regulation at the modular and systems level for basic and applied research.

Methodology:

Apply Independent Component Analysis (ICA) to high-quality bacterial transcriptomic datasets to identify iModulons, infer their activities across environmental conditions, and associate iModulons with known genetic regulators.