A community resource for paired genomic and metabolomic data mining

Schorn, Michelle A.; Verhoeven, Stefan; Ridder, Lars; Huber, Florian; Acharya, Deepa; Aksenov, Alexander A.; Aleti, Gajender; Moghaddam, Jamshid Amiri; Aron, Allegra T.; Aziz, Saefuddin; Bauermeister, Anelize; Bauman, Katherine D.; Baunach, Martin; Beemelmanns, Christine; Beman, J. Michael; Berlanga-Clavero, María Victoria; Blacutt, Alex A.; Bode, Helge B.; Boullié, A.; Brejnrod, Asker; Bugni, Tim S.; Calteau, Alexandra; Cao, Liu; Carrión, Víctor J.; Castelo-Branco, Raquel; Chanana, Shaurya; Chase, Alexander B.; Chevrette, Marc G.; Costa-Lotufo, Letı́cia V.; Crawford, Jason M.; Currie, Cameron R.; Cuypers, Bart; Dang, Tam; Rond, Tristan de; Demko, Alyssa M.; Dittmann, Elke; Du, Congcong; Drozd, Christopher; Dujardin, Jean Claude; Dutton, Rachel J.; Edlund, Anna; Fewer, David P.; Garg, Neha; Gauglitz, Julia M.; Gentry, Emily C.; Gerwick, Lena; Glukhov, Evgenia; Gross, Harald; Gugger, Muriel; Matus, Dulce G Guillén; Helfrich, Eric J. N.; Hempel, Benjamin-Florian; Hur, Jae Seoun; Iorio, Marianna; Jensen, Paul R.; Kang, Kyo Bin; Kaysser, Leonard; Kelleher, Neil L.; Kim, Chung Sub; Kim, Ki‐Hyun; Koester, Irina; König, Gabriele M.; Leão, Tiago; Lee, Seoung Rak; Lee, Yi Yuan; Li, Xuanji; Little, Jessica C; Maloney, Katherine N.; Männle, Daniel; H, Christian Martin; McAvoy, Andrew C.; Metcalf, Willam W.; Mohimani, Hosein; Molina-Santiago, Carlos; Moore, Bradley S.; Mullowney, Michael W.; Muskat, Mitchell N.; Nothias, Louis Félix; O’Neill, Ellis C.; Parkinson, Elizabeth I.; Petras, Daniel; Piel, Jörn; Pierce, Emily C; Pires, Karine; Reher, Raphael; Romero, Diego; Roper, M. Caroline; Rust, Michael; Saad, Hamada; Saenz, Carmen; Sanchez, Laura M.; Sørensen, Søren J.; Sosio, Margherita; Süßmuth, Roderich D.; Sweeney, Douglas; Tahlan, Kapil; Thomson, Regan J.; Tobias, Nicholas J.; Trindade-Silva, Amaro E.; Wezel, Gilles P. van; Wang, Mingxun; Weldon, Kelly C.; Zhang, Fan; Ziemert, Nadine; Duncan, Katherine R.; Crüsemann, Max; Rogers, Simon; Dorrestein, Pieter C.; Medema, Marnix H.; Hooft, Justin J. J. van der

doi:10.1038/s41589-020-00724-z

Cited by 91 publications

(69 citation statements)

References 25 publications

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“…For mass spectra datasets, we selected 46 high-resolution GNPS spectral datasets (about 8 million spectra in total) with paired genomic/metagenomic data available 39 for large-scale spectral searching and secondary metabolites identification. Moreover, we selected spectral datasets from various environment, including MSV000084092 (~57,000 spectra from human serum), MSV000086427 (~209,000 spectra from 38 plant species) and MSV000079450 (~400,000 spectra from Pseudomonas isolates).…”

Section: Resultsmentioning

confidence: 99%

MolDiscovery: learning mass spectrometry fragmentation of small molecules

et al. 2021

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Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

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Section: Resultsmentioning

confidence: 99%

MolDiscovery: learning mass spectrometry fragmentation of small molecules

et al. 2021

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“…In contrast to genes and proteins, metabolites have much greater structural diversity: they are not simply combinations of 4-20 letters of the gene or protein alphabet. The developments and combinations of novel metabolomics approaches and bioinformatics pipelines to search multiple databases for the identification of compounds in a metabolomics profile are crucial and urgently needed (e.g., [76,77]).…”

Section: Challenges Opportunities and Future Directionsmentioning

confidence: 99%

The Chemistry of Stress: Understanding the ‘Cry for Help’ of Plant Roots

et al. 2021

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Plants are faced with various biotic and abiotic stresses during their life cycle. To withstand these stresses, plants have evolved adaptive strategies including the production of a wide array of primary and secondary metabolites. Some of these metabolites can have direct defensive effects, while others act as chemical cues attracting beneficial (micro)organisms for protection. Similar to aboveground plant tissues, plant roots also appear to have evolved “a cry for help” response upon exposure to stress, leading to the recruitment of beneficial microorganisms to help minimize the damage caused by the stress. Furthermore, emerging evidence indicates that microbial recruitment to the plant roots is, at least in part, mediated by quantitative and/or qualitative changes in root exudate composition. Both volatile and water-soluble compounds have been implicated as important signals for the recruitment and activation of beneficial root-associated microbes. Here we provide an overview of our current understanding of belowground chemical communication, particularly how stressed plants shape its protective root microbiome.

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“…NPLinker accepts genomic outputs from antiSMASH and BiG-SCAPE (including reference BGCs from the MIBiG database [ 32 ]), and metabolomic output from the public, community-driven Global Natural Products Social (GNPS) knowledge base [ 33 ]. Additionally, it includes integration with the Paired omics Data Platform [ 34 ] to retrieve paired public genomics and metabolomics data ( https://pairedomicsdata.bioinformatics.nl ).…”

Section: Methodsmentioning

confidence: 99%

“…Recognising the difficulty of obtaining ground truth data in this field, Schorn et al . recently developed the Paired Omics Data Platform documenting the location of genomic and metabolomic data sets from microbial experiments, with a focus on data sets with BGCs and MS2 spectra [ 34 ]. This gives a repository of validated links in various data sets, citing the articles in which the links were validated, which can then be used to evaluate scoring methods for prospective links between BGCs and spectra, in terms of relative over-representation of validated links towards the upper end of the distribution of scores.…”

Section: Methodsmentioning

confidence: 99%

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

et al. 2021

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Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

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A community resource for paired genomic and metabolomic data mining

Cited by 91 publications

References 25 publications

MolDiscovery: learning mass spectrometry fragmentation of small molecules

MolDiscovery: learning mass spectrometry fragmentation of small molecules

The Chemistry of Stress: Understanding the ‘Cry for Help’ of Plant Roots

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions

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