PanDelos-frags
PanDelos-frags reconstructs gene families from incomplete genomic and metagenomic sequence fragments to infer missing genetic information and enable gene-oriented pangenome and population-level evolutionary analyses.
Key Features:
- Handling Incomplete Genomes: Leverages sequence fragments from incomplete genomes to infer missing genetic information.
- Gene Family Reconstruction: Calculates sequence homology among genes to reconstruct gene families and characterize their composition and putative functions.
- Integration with Metagenomics: Analyzes gene-oriented pangenomes within metagenomic datasets, including human microbiome studies, to investigate ecosystem functions and population-level evolution.
- Performance and Validation: Demonstrated superior performance compared to state-of-the-art methods and validated through synthetic benchmarks and real-world metagenomic applications.
Scientific Applications:
- Microbial genomics: Reconstruction of gene families from fragmented genomes to study microbial diversity and adaptation.
- Metagenomic studies: Analysis of human microbiome and other complex ecosystems using fragmentary sequence data.
- Population-level evolutionary analysis: Investigation of gene-oriented pangenomes to assess population structure and evolutionary trends.
- Functional inference: Derivation of putative gene functions and community-level functional profiles from incomplete sequences.
Methodology:
Infers missing genetic information from sequence fragments, calculates sequence homology among genes to reconstruct gene families, and analyzes gene-oriented pangenomes.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Programming Languages:
- Java, Python, Shell
- Added:
- 5/3/2024
- Last Updated:
- 11/24/2024
Operations
Publications
Bonnici V, Mengoni C, Mangoni M, Franco G, Giugno R. PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies. Journal of Biomedical Informatics. 2023;148:104552. doi:10.1016/j.jbi.2023.104552. PMID:37995844.
PMID: 37995844