Fibromine

Fibromine integrates manually curated bulk transcriptomic and proteomic datasets and single-cell RNA sequencing data to support target discovery and hypothesis generation in idiopathic pulmonary fibrosis (IPF) research.

Key Features:

• Integrated Database: Consolidates manually curated transcriptomic and proteomic datasets from human patients and animal models relevant to pulmonary fibrosis.
• Dataset Benchmarking: Implements a novel benchmarking system that accredits transcriptomic datasets based on transcriptomic dataset characteristics to guide dataset selection.
• scRNA-seq Integration: Integrates multiple single-cell RNA sequencing (scRNA-seq) datasets to enable cell-specific gene expression analyses.
• Multi-omics Integration: Links bulk transcriptomic and proteomic data for comparative multi-omics analyses.
• Implementation Platform: Implemented as an R Shiny application.

Scientific Applications:

• Target discovery and hypothesis generation: Supports identification of candidate molecular targets and generation of research hypotheses in pulmonary fibrosis using integrated datasets.
• Mechanistic investigation of IPF: Enables comparative analysis to dissect pathogenetic mechanisms underlying idiopathic pulmonary fibrosis.
• Comparative dataset analysis and validation: Facilitates cross-study comparisons of publicly available transcriptomic and proteomic datasets for hypothesis validation.
• Multi-level disease characterization: Provides combined bulk transcriptomic, proteomic, and single-cell perspectives to characterize disease processes at multiple biological levels.

Methodology:

Manual curation of transcriptomic and proteomic datasets; accreditation/benchmarking of transcriptomic datasets based on dataset characteristics; integration of bulk transcriptomic, proteomic, and scRNA-seq datasets; implemented using R Shiny.