Abstract

A common problem in the annotation of open reading frames (ORFs) is the identification of genes that are functionally similar but have limited or no sequence homology. This is particularly the case for bacteriocins, a very diverse group of antimicrobial peptides produced by bacteria and usually encoded by small, poorly conserved ORFs. ORFs surrounding bacteriocin genes are often biosynthetic genes. This information can be used to locate putative structural bacteriocin genes. Here, we describe BAGEL, a web server that identifies putative bacteriocin ORFs in a DNA sequence using novel, knowledge-based bacteriocin databases and motif databases. Many bacteriocins are encoded by small genes that are often omitted in the annotation process of bacterial genomes. Thus, we have implemented ORF detection using a number of published ORF prediction tools. In addition, BAGEL takes into account the genomic context, i.e. for each potential bacteriocin-encoding ORF, the sequence of the surrounding region on the genome is analyzed for genes that might encode proteins involved in biosynthesis, transport, regulation and/or immunity. These innovations make BAGEL unique in its ability to detect putative bacteriocin gene clusters in (new) bacterial genomes. BAGEL is freely accessible at: http://bioinformatics.biol.rug.nl/websoftware/bagel.