BAGEL4

BAGEL4 identifies gene clusters encoding ribosomally synthesized and posttranslationally modified peptides (RiPPs) and bacteriocins in bacterial and metagenomic DNA to enable discovery and analysis of secondary metabolites.

Key Features:

• Gene cluster mining: Identifies gene clusters associated with RiPPs and bacteriocins in bacterial and metagenomic DNA.
• Core-peptide database and HMM motifs: Uses an updated core-peptide database and hidden Markov model (HMM) motifs in context genes to detect candidate peptides and biosynthetic loci.
• ORF-calling independence and speed: Operates independently of open reading frame (ORF) calling and provides enhanced processing speed compared with its predecessor.
• Database integration: Integrates databases enriched with literature references and links to UniProt and NCBI resources.
• Context gene annotation: Annotates context genes via rapid BLAST searches against the prokaryote section of the UniRef90 database.
• Regulatory prediction: Performs automated promoter and terminator prediction for regulatory element identification.
• RNA expression integration: Accepts uploaded RNA expression data and displays it alongside identified clusters to provide transcriptional context.
• Enhanced web-BLAST structural loading: Enhanced web-BLAST dynamically loads structural information, including internal cross-linking data, from UniProt.

Scientific Applications:

• Novel compound discovery: Enables discovery and annotation of novel RiPPs and bacteriocins from genomic and metagenomic datasets.
• Functional and regulatory analysis: Supports analysis of biosynthetic context genes and regulatory elements using HMM motifs, promoter/terminator prediction, and RNA expression data.
• Applied-research prioritization: Facilitates selection of candidate secondary metabolites for further investigation in medicine, agriculture, and biotechnology.

Methodology:

Computational methods include database mining with an updated core-peptide database, HMM motif detection in context genes, BLAST searches (including rapid BLAST against the prokaryote UniRef90), automated promoter and terminator prediction, integration of RNA expression data, and dynamic loading of structural/internal cross-linking information from UniProt.