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      The confluence of big data and evolutionary genome mining for the discovery of natural products

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          Abstract

          The development and application of evolutionary genome mining tools and databases has propelled natural products research into the modern age of Big Data.

          Abstract

          This review covers literature between 2003–2021

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          Most cited references149

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          Basic local alignment search tool.

          Stephen F Altschul, Warren Gish, Webb Miller … (1990)
          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            ResFinder 4.0 for predictions of phenotypes from genotypes

            Valeria Bortolaia, Rolf S. Kaas, Etienne Ruppé … (2020)
            Abstract Objectives WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. Methods The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. Results Genotype–phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype–phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. Conclusions WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered.
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              Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

              Mingxun Wang, Jeremy J. Carver, Vanessa V Phelan … (2016)
              The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.
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                Author and article information

                Contributors
                Marc G. Chevrette: (View ORCID Profile)
                Athina Gavrilidou: (View ORCID Profile)
                Shrikant Mantri: (View ORCID Profile)
                Nelly Selem-Mojica: (View ORCID Profile)
                Nadine Ziemert: (View ORCID Profile)
                Francisco Barona-Gómez: (View ORCID Profile)
                Journal
                Journal ID (publisher-id): NPRRDF
                Title: Natural Product Reports
                Abbreviated Title: Nat. Prod. Rep.
                Publisher: Royal Society of Chemistry (RSC)
                ISSN (Print): 0265-0568
                ISSN (Electronic): 1460-4752
                Publication date Created: November 17 2021
                Publication date (Electronic): 2021
                Volume: 38
                Issue: 11
                Pages: 2024-2040
                Affiliations
                [1 ]Wisconsin Institute for Discovery, Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI, USA
                [2 ]Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of Tübingen, Germany
                [3 ]German Centre for Infection Research (DZIF), Partner Site Tübingen, Germany
                [4 ]Computational Biology Laboratory, National Agri-Food Biotechnology Institute (NABI), Mohali, Punjab, India
                [5 ]Laboratorio de Evolución de la Diversidad Metabólica, Unidad de Genómica Avanzada (Langebio), Cinvestav-IPN, Irapuato, Guanajuato, Mexico
                Article
                DOI: 10.1039/D1NP00013F
                PubMed ID: 34787598
                SO-VID: 5087e13a-efa7-4f12-9f3b-59db913a8bb1
                Copyright © © 2021
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