PlanGexQ
PlanGexQ encodes planarian morphologies as mathematical graphs and registers gene expression patterns to produce ontology-linked standardized datasets for curation and mechanistic analysis.
Key Features:
- Morphology encoding: Encodes reference morphologies using mathematical graphs linked to the planarian anatomy ontology.
- Reference morphology generation: Automatically generates reference morphologies from encoded graphs and ontology terms.
- Gene expression registration: Registers gene expression pattern images to the encoded reference morphologies for spatial mapping.
- Ontology-based annotation: Automatically generates annotations using planarian anatomy ontology terms by analyzing spatial expression patterns and associated textual descriptions.
- Standardized dataset creation: Encodes morphological outcomes and gene expression patterns into a centralized, standardized format to support knowledge extraction and mechanistic model development.
Scientific Applications:
- Spatial phenotype formalization: Formalizes spatial phenotypes and gene expression patterns for comparative analysis and data integration in developmental and regenerative biology.
- Mechanistic model support: Provides standardized, ontology-linked datasets to support reverse-engineering and development of mechanistic models.
- Perturbation analysis: Enables unambiguous description and interpretation of experimental outcomes from genetic, surgical, or pharmacological perturbations in planarians and other regenerative organisms.
Methodology:
Worm morphologies are encoded as mathematical graphs tied to anatomical ontology terms; reference morphologies are automatically generated from these encodings; gene expression patterns are registered to the reference morphologies and integrated into a centralized standardized dataset.
Topics
Details
- Added:
- 1/18/2021
- Last Updated:
- 1/24/2021
Operations
Publications
Roy J, Cheung E, Bhatti J, Muneem A, Lobo D. Curation and annotation of planarian gene expression patterns with segmented reference morphologies. Bioinformatics. 2020;36(9):2881-2887. doi:10.1093/bioinformatics/btaa023. PMID:31950976.