Signaling Pathways Project

Signaling Pathways Project integrates public, manually curated transcriptomic and cistromic (ChIP-Seq) datasets to produce consensus omics signatures and mappings that support hypothesis generation and validation about mammalian cellular signaling pathways.

Key Features:

• Data integration: Integrates public, manually curated transcriptomic and cistromic datasets, including ChIP-Seq.
• Experiment mapping: Maps over 10,000 public transcriptomic or cistromic experiments to specific pathway nodes or biosamples.
• Node classifications: Incorporates community classifications of pathway nodes—receptors, enzymes, transcription factors, and co-nodes—and their associated bioactive small molecules.
• Consensomes: Computes consensomes that rank genes by significant differential expression or promoter occupancy across experiments mapped to particular node families.
• Gene-level integration: Integrates transcriptomic and cistromic evidence at the gene target level.
• Manipulation linkage: Links datasets to genetic and small molecule manipulations of receptors, enzymes, and transcription factors.
• Validation: Validates consensomes through alignment with canonical literature and experimental bench confirmation of regulatory relationships.

Scientific Applications:

• Hypothesis generation and validation: Enables generation and validation of hypotheses about mammalian cellular signaling pathways by aggregating multi-omics evidence.
• Gene target prioritization: Prioritizes gene targets regulated by specific pathway node families using consensome rankings.
• Regulatory relationship discovery: Facilitates identification of previously uncharacterized regulatory relationships between pathway nodes and gene targets through integrated transcriptomic and cistromic analysis.
• Context-specific analysis: Supports contextual analysis of signaling by mapping experiments to biosamples and specific node perturbations.

Methodology:

Integrates manually curated transcriptomic and cistromic (ChIP-Seq) datasets, maps experiments to pathway nodes and biosamples, computes consensomes that rank genes by significant differential expression or promoter occupancy across experiments mapped to node families, integrates transcriptomic and cistromic evidence at the gene target level, and uses community classifications of pathway nodes and bioactive small molecules.