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Bioinformatics. 2017 Oct 15;33(20):3202-3210. doi: 10.1093/bioinformatics/btx400.

SANDPUMA: ensemble predictions of nonribosomal peptide chemistry reveal biosynthetic diversity across Actinobacteria.

Author information

1
Department of Genetics.
2
Department of Bacteriology and J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53706, USA.
3
Applied Bioinformatics, Department of Computer Science, Quantitative Biology Center and Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany.
4
Biomolecular Interactions, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
5
Bioinformatics Group, Wageningen University, 6708PB Wageningen, The Netherlands.

Abstract

Summary:

Nonribosomally synthesized peptides (NRPs) are natural products with widespread applications in medicine and biotechnology. Many algorithms have been developed to predict the substrate specificities of nonribosomal peptide synthetase adenylation (A) domains from DNA sequences, which enables prioritization and dereplication, and integration with other data types in discovery efforts. However, insufficient training data and a lack of clarity regarding prediction quality have impeded optimal use. Here, we introduce prediCAT, a new phylogenetics-inspired algorithm, which quantitatively estimates the degree of predictability of each A-domain. We then systematically benchmarked all algorithms on a newly gathered, independent test set of 434 A-domain sequences, showing that active-site-motif-based algorithms outperform whole-domain-based methods. Subsequently, we developed SANDPUMA, a powerful ensemble algorithm, based on newly trained versions of all high-performing algorithms, which significantly outperforms individual methods. Finally, we deployed SANDPUMA in a systematic investigation of 7635 Actinobacteria genomes, suggesting that NRP chemical diversity is much higher than previously estimated. SANDPUMA has been integrated into the widely used antiSMASH biosynthetic gene cluster analysis pipeline and is also available as an open-source, standalone tool.

Availability and implementation:

SANDPUMA is freely available at https://bitbucket.org/chevrm/sandpuma and as a docker image at https://hub.docker.com/r/chevrm/sandpuma/ under the GNU Public License 3 (GPL3).

Contact:

chevrette@wisc.edu or marnix.medema@wur.nl.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28633438
PMCID:
PMC5860034
DOI:
10.1093/bioinformatics/btx400
[Indexed for MEDLINE]
Free PMC Article

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