MapGL
MapGL infers evolutionary gain and loss of short genomic sequence features to enable phylogenetic analysis of cis-regulatory element turnover across eutherian genomes.
Key Features:
- Targeted sequence features: Infers gain and loss events for short genomic sequence features, specifically cis-regulatory elements.
- Phylogenetic algorithm: Implements phylogenetic maximum parsimony for ancestral-state reconstruction.
- Ancestral-state reconstruction: Reconstructs ancestral presence/absence states by minimizing the number of evolutionary changes.
- Phylogenetic scope: Supports analysis across a wide range of phylogenetic topologies and evolutionary distances.
- Taxonomic application: Applied to comparative analyses of eutherian genomes.
- Evolutionary context: Places species-specific sequence features within an explicit phylogenetic framework to assess regulatory sequence turnover.
Scientific Applications:
- Comparative genomics: Elucidates shared and divergent regulatory sequence content between species using phylogenetic inference.
- Regulatory sequence turnover: Quantifies gain and loss of cis-regulatory elements to study turnover dynamics.
- Phenotypic divergence: Investigates how genomic sequence gain and loss contribute to species-specific phenotypic differences.
- Genome evolution and regulatory biology: Supports studies on genome evolution, regulatory element evolution, and the genetic basis of adaptation.
Methodology:
Uses phylogenetic maximum parsimony to reconstruct ancestral states by minimizing the number of evolutionary changes.
Topics
Details
- License:
- MIT
- Tool Type:
- command-line tool, library
- Programming Languages:
- Python, Shell, R
- Added:
- 1/18/2021
- Last Updated:
- 2/19/2021
Operations
Publications
Diehl AG, Boyle AP. MapGL: inferring evolutionary gain and loss of short genomic sequence features by phylogenetic maximum parsimony. BMC Bioinformatics. 2020;21(1). doi:10.1186/s12859-020-03742-9. PMID:32962625. PMCID:PMC7510305.
PMID: 32962625
PMCID: PMC7510305
Funding: - Alfred P. Sloan Foundation: FG-2015-65465
- National Institutes of Health: DBI-1651614