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.