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. 2013 Jul;41(Web Server issue):W448-53.
doi: 10.1093/nar/gkt391. Epub 2013 May 15.

BAGEL3: Automated identification of genes encoding bacteriocins and (non-)bactericidal posttranslationally modified peptides

Auke J van Heel  1 , Anne de Jong, Manuel Montalbán-López, Jan Kok, Oscar P Kuipers
Affiliations

BAGEL3: Automated identification of genes encoding bacteriocins and (non-)bactericidal posttranslationally modified peptides

Auke J van Heel et al. Nucleic Acids Res. 2013 Jul.

Abstract

Identifying genes encoding bacteriocins and ribosomally synthesized and posttranslationally modified peptides (RiPPs) can be a challenging task. Especially those peptides that do not have strong homology to previously identified peptides can easily be overlooked. Extensive use of BAGEL2 and user feedback has led us to develop BAGEL3. BAGEL3 features genome mining of prokaryotes, which is largely independent of open reading frame (ORF) predictions and has been extended to cover more (novel) classes of posttranslationally modified peptides. BAGEL3 uses an identification approach that combines direct mining for the gene and indirect mining via context genes. Especially for heavily modified peptides like lanthipeptides, sactipeptides, glycocins and others, this genetic context harbors valuable information that is used for mining purposes. The bacteriocin and context protein databases have been updated and it is now easy for users to submit novel bacteriocins or RiPPs. The output has been simplified to allow user-friendly analysis of the results, in particular for large (meta-genomic) datasets. The genetic context of identified candidate genes is fully annotated. As input, BAGEL3 uses FASTA DNA sequences or folders containing multiple FASTA formatted files. BAGEL3 is freely accessible at http://bagel.molgenrug.nl.

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Figures

Figure 1.
Figure 1.
Schematic overview of the BAGEL3 genome mining procedure. BAGEL3 uses two different approaches in parallel to find bacteriocins and modified peptides. Both approaches use nucleotide sequences in FASTA format as input. The first approach (left, red) describes how the context-based approach proceeds. The second approach (right, blue) describes the simpler precursor peptide-based mining. Finally, both methods generate a single summary table with links to detailed graphical reports.
Figure 2.
Figure 2.
Example detailed report of a lantibiotic cluster encoding a nisin variant and its modification enzymes found in Streptococcus suis J14 (NC_017618.1) using BAGEL3. The target gene (smallORF_6) was in this case identified by the specialized small ORF calling procedure.
See this image and copyright information in PMC

References

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