BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins
By A. J. Heel, Anne de Jong, Chunxu Song, Jakob H. Viel, Jan Kok, Oscar P. Kuipers. Nucleic Acids Res., 2018
955 citations recorded by the source corpus.
Abstract Interest in secondary metabolites such as RiPPs (ribosomally synthesized and posttranslationally modified peptides) is increasing worldwide. To facilitate the research in this field we have updated our mining web server. BAGEL4 is faster than its predecessor and is now fully independent from ORF-calling. Gene clusters of interest are discovered using the core-peptide database and/or through HMM motifs that are present in associated context genes. The databases used for mining have been updated and extended with literature references and links to UniProt and NCBI. Additionally, we have included automated promoter and terminator prediction and the option to upload RNA expression data, which can be displayed along with the identified clusters. Further improvements include the annotation of the context genes, which is now based on a fast blast against the prokaryote part of the UniRef90 database, and the improved web-BLAST feature that dynamically loads structural data such as internal cross-linking from UniProt. Overall BAGEL4 provides the user with more information through a user-friendly web-interface which simplifies data evaluation. BAGEL4 is freely accessible at http://bagel4.molgenrug.nl.
Subfields: Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/42b33207d8b8636a36eb5cffc2ab7343f4dd90ac
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Citations in the corpus
- antiSMASH 6.0: improving cluster detection and comparison capabilities (2021, methodology): “To identify these biosynthetic functions, antiSMASH uses profile hidden Markov models (pHMMs) from PFAM (26), TIGRFAMs (27), SMART (28), BAGEL (3), Yadav et al. (29), or custom models. antiSMASH 5 contained rules for 58 different BGC types (15), version 6 increases this number to 71.”