BAGEL3

BAGEL3 identifies genes encoding bacteriocins and ribosomally synthesized and posttranslationally modified peptides (RiPPs) in prokaryotic genomes and metagenomes.

Key Features:

• Genome mining independent of ORF predictions: Performs genome mining of prokaryotes largely independent of open reading frame (ORF) predictions to detect genes that may not conform to known gene structures.
• Direct and indirect mining approaches: Combines direct mining of candidate genes with indirect, context-gene–based mining to improve detection of peptides with limited homology.
• Detection of modified peptide classes: Detects heavily post-translationally modified peptide classes including lanthipeptides, sactipeptides, and glycocins.
• Updated databases: Utilizes updated bacteriocin and context protein databases to recognize a broader spectrum of peptides.
• Input formats and scalability: Accepts single or multiple FASTA-formatted files (including folders of FASTA files) and is applicable to large metagenomic datasets.
• Annotated genetic contexts: Produces fully annotated genetic contexts for identified candidate genes to support downstream interpretation.

Scientific Applications:

• Genome- and metagenome-scale peptide discovery: Enables discovery of bacteriocins and RiPPs across prokaryotic genomes and metagenomes.
• Antimicrobial peptide research: Supports identification of candidate antimicrobial peptides for functional and therapeutic studies.
• Microbial diversity and function analysis: Facilitates discovery of novel peptides to expand understanding of microbial diversity and the functional roles of bacteriocins and RiPPs.

Methodology:

Integrates direct gene mining with context-based indirect approaches, performs genome mining largely independent of ORF predictions, and uses updated bacteriocin and context protein databases to identify genes encoding bacteriocins and RiPPs.