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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Nature biotechnology

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG.
PMID: 31341288
Nat Biotechnol. 2019 Aug;37(8):852-857. doi: 10.1038/s41587-019-0209-9.

No abstract available.

Cite
Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852-857doi: 10.1038/s41587-019-0209-9.
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., Cope, E. K., Da Silva, R., Diener, C., Dorrestein, P. C., Douglas, G. M., Durall, D. M., Duvallet, C., Edwardson, C. F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J. M., Gibbons, S. M., Gibson, D. L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G. A., Janssen, S., Jarmusch, A. K., Jiang, L., Kaehler, B. D., Kang, K. B., Keefe, C. R., Keim, P., Kelley, S. T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M. G. I., Lee, J., Ley, R., Liu, Y. X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B. D., McDonald, D., McIver, L. J., Melnik, A. V., Metcalf, J. L., Morgan, S. C., Morton, J. T., Naimey, A. T., Navas-Molina, J. A., Nothias, L. F., Orchanian, S. B., Pearson, T., Peoples, S. L., Petras, D., Preuss, M. L., Pruesse, E., Rasmussen, L. B., Rivers, A., Robeson, M. S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S. J., Spear, J. R., Swafford, A. D., Thompson, L. R., Torres, P. J., Trinh, P., Tripathi, A., Turnbaugh, P. J., Ul-Hasan, S., van der Hooft, J. J. J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K. C., Williamson, C. H. D., Willis, A. D., Xu, Z. Z., Zaneveld, J. R., Zhang, Y., Zhu, Q., Knight, R., & Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology, 37(8), 852-857. https://doi.org/10.1038/s41587-019-0209-9
Bolyen, Evan, et al. "Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2." Nature biotechnology vol. 37,8 (2019): 852-857. doi: https://doi.org/10.1038/s41587-019-0209-9
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019 Aug;37(8):852-857. doi: 10.1038/s41587-019-0209-9. PMID: 31341288; PMCID: PMC7015180.
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High resolution techniques: general discussion.

Faraday discussions

Adair E, Afonso C, Bell NGA, Davies AN, Delsuc MA, Godfrey R, Goodacre R, Hawkes JA, Hertkorn N, Jones D, Lameiras P, Le Guennec A, Lubben A, Nilsson M, Paša-Tolić L, Richards J, Rodgers RP, Rüger CP, Schmitt-Kopplin P, Schoenmakers PJ, Sidebottom P, Staerk D, Summerfield S, Uhrín D, van Delft P, van der Hooft JJJ, van Zelst FHM, Zherebker A.
PMID: 31369012
Faraday Discuss. 2019 Aug 15;218:247-267. doi: 10.1039/c9fd90045d.

No abstract available.

Cite
Adair E, Afonso C, Bell NGA, et al. High resolution techniques: general discussion. Faraday Discuss. 2019;218:247-267doi: 10.1039/c9fd90045d.
Adair, E., Afonso, C., Bell, N. G. A., Davies, A. N., Delsuc, M. A., Godfrey, R., Goodacre, R., Hawkes, J. A., Hertkorn, N., Jones, D., Lameiras, P., Le Guennec, A., Lubben, A., Nilsson, M., Paša-Tolić, L., Richards, J., Rodgers, R. P., Rüger, C. P., Schmitt-Kopplin, P., Schoenmakers, P. J., Sidebottom, P., Staerk, D., Summerfield, S., Uhrín, D., van Delft, P., van der Hooft, J. J. J., van Zelst, F. H. M., & Zherebker, A. (2019). High resolution techniques: general discussion. Faraday discussions, 218247-267. https://doi.org/10.1039/c9fd90045d
Adair, Elaine, et al. "High resolution techniques: general discussion." Faraday discussions vol. 218 (2019): 247-267. doi: https://doi.org/10.1039/c9fd90045d
Adair E, Afonso C, Bell NGA, Davies AN, Delsuc MA, Godfrey R, Goodacre R, Hawkes JA, Hertkorn N, Jones D, Lameiras P, Le Guennec A, Lubben A, Nilsson M, Paša-Tolić L, Richards J, Rodgers RP, Rüger CP, Schmitt-Kopplin P, Schoenmakers PJ, Sidebottom P, Staerk D, Summerfield S, Uhrín D, van Delft P, van der Hooft JJJ, van Zelst FHM, Zherebker A. High resolution techniques: general discussion. Faraday Discuss. 2019 Aug 15;218:247-267. doi: 10.1039/c9fd90045d. PMID: 31369012.
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Phenylpropane as an Alternative Dearomatizing Unit of Indoles: Discovery of Inaequalisines A and B Using Substructure-Informed Molecular Networking.

Organic letters

Cauchie G, N'Nang EO, van der Hooft JJJ, Le Pogam P, Bernadat G, Gallard JF, Kumulungui B, Champy P, Poupon E, Beniddir MA.
PMID: 32686942
Org Lett. 2020 Aug 07;22(15):6077-6081. doi: 10.1021/acs.orglett.0c02153. Epub 2020 Jul 20.

Inaequalisines A and B (

Cite
Cauchie G, N'Nang EO, van der Hooft JJJ, et al. Phenylpropane as an Alternative Dearomatizing Unit of Indoles: Discovery of Inaequalisines A and B Using Substructure-Informed Molecular Networking. Org Lett. 2020;22(15):6077-6081doi: 10.1021/acs.orglett.0c02153.
Cauchie, G., N'Nang, E. O., van der Hooft, J. J. J., Le Pogam, P., Bernadat, G., Gallard, J. F., Kumulungui, B., Champy, P., Poupon, E., & Beniddir, M. A. (2020). Phenylpropane as an Alternative Dearomatizing Unit of Indoles: Discovery of Inaequalisines A and B Using Substructure-Informed Molecular Networking. Organic letters, 22(15), 6077-6081. https://doi.org/10.1021/acs.orglett.0c02153
Cauchie, Gaëla, et al. "Phenylpropane as an Alternative Dearomatizing Unit of Indoles: Discovery of Inaequalisines A and B Using Substructure-Informed Molecular Networking." Organic letters vol. 22,15 (2020): 6077-6081. doi: https://doi.org/10.1021/acs.orglett.0c02153
Cauchie G, N'Nang EO, van der Hooft JJJ, Le Pogam P, Bernadat G, Gallard JF, Kumulungui B, Champy P, Poupon E, Beniddir MA. Phenylpropane as an Alternative Dearomatizing Unit of Indoles: Discovery of Inaequalisines A and B Using Substructure-Informed Molecular Networking. Org Lett. 2020 Aug 07;22(15):6077-6081. doi: 10.1021/acs.orglett.0c02153. Epub 2020 Jul 20. PMID: 32686942.
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Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery.

mSystems

Louwen JJR, van der Hooft JJJ.
PMID: 34427506
mSystems. 2021 Aug 31;6(4):e0072621. doi: 10.1128/mSystems.00726-21. Epub 2021 Aug 24.

Microbial specialized metabolites are key mediators in host-microbiome interactions. Most of the chemical space produced by the microbiome currently remains unexplored and uncharacterized. This situation calls for new and improved methods to exploit the growing publicly available genomic and...

Cite
Louwen JJR, van der Hooft JJJ. Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery. mSystems. 2021;6(4):e0072621doi: 10.1128/mSystems.00726-21.
Louwen, J. J. R., & van der Hooft, J. J. J. (2021). Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery. mSystems, 6(4), e0072621. https://doi.org/10.1128/mSystems.00726-21
Louwen, Joris J R, and van der Hooft, Justin J J. "Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery." mSystems vol. 6,4 (2021): e0072621. doi: https://doi.org/10.1128/mSystems.00726-21
Louwen JJR, van der Hooft JJJ. Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery. mSystems. 2021 Aug 31;6(4):e0072621. doi: 10.1128/mSystems.00726-21. Epub 2021 Aug 24. PMID: 34427506; PMCID: PMC8407348.
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NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.

Journal of natural products

Kim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, van der Hooft JJJ, Dorrestein PC, Gerwick WH, Cottrell GW.
PMID: 34662515
J Nat Prod. 2021 Nov 26;84(11):2795-2807. doi: 10.1021/acs.jnatprod.1c00399. Epub 2021 Oct 18.

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal...

Cite
Kim HW, Wang M, Leber CA, et al. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. J Nat Prod. 2021;84(11):2795-2807doi: 10.1021/acs.jnatprod.1c00399.
Kim, H. W., Wang, M., Leber, C. A., Nothias, L. F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021). NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. Journal of natural products, 84(11), 2795-2807. https://doi.org/10.1021/acs.jnatprod.1c00399
Kim, Hyun Woo, et al. "NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products." Journal of natural products vol. 84,11 (2021): 2795-2807. doi: https://doi.org/10.1021/acs.jnatprod.1c00399
Kim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, van der Hooft JJJ, Dorrestein PC, Gerwick WH, Cottrell GW. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. J Nat Prod. 2021 Nov 26;84(11):2795-2807. doi: 10.1021/acs.jnatprod.1c00399. Epub 2021 Oct 18. PMID: 34662515; PMCID: PMC8631337.
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Future challenges and new approaches: general discussion.

Faraday discussions

Afonso C, Chaux C, Davies AN, Delsuc MA, Fernandez Lima F, Gauchotte-Lindsay C, Giusti P, Goodacre R, Hawkes JA, Hertkorn N, Jansen JJ, Kew W, Kuhn S, Lubben A, McGill D, Nilsson M, Parkinson J, Rodgers RP, Rogers S, Schmitt-Kopplin P, Schoenmakers PJ, Shintu L, Soong R, Summerfield S, Surman A, Uhrín D, van der Hooft JJJ.
PMID: 31380556
Faraday Discuss. 2019 Aug 15;218:505-523. doi: 10.1039/c9fd90046b.

No abstract available.

Cite
Afonso C, Chaux C, Davies AN, et al. Future challenges and new approaches: general discussion. Faraday Discuss. 2019;218:505-523doi: 10.1039/c9fd90046b.
Afonso, C., Chaux, C., Davies, A. N., Delsuc, M. A., Fernandez Lima, F., Gauchotte-Lindsay, C., Giusti, P., Goodacre, R., Hawkes, J. A., Hertkorn, N., Jansen, J. J., Kew, W., Kuhn, S., Lubben, A., McGill, D., Nilsson, M., Parkinson, J., Rodgers, R. P., Rogers, S., Schmitt-Kopplin, P., Schoenmakers, P. J., Shintu, L., Soong, R., Summerfield, S., Surman, A., Uhrín, D., & van der Hooft, J. J. J. (2019). Future challenges and new approaches: general discussion. Faraday discussions, 218505-523. https://doi.org/10.1039/c9fd90046b
Afonso, Carlos, et al. "Future challenges and new approaches: general discussion." Faraday discussions vol. 218 (2019): 505-523. doi: https://doi.org/10.1039/c9fd90046b
Afonso C, Chaux C, Davies AN, Delsuc MA, Fernandez Lima F, Gauchotte-Lindsay C, Giusti P, Goodacre R, Hawkes JA, Hertkorn N, Jansen JJ, Kew W, Kuhn S, Lubben A, McGill D, Nilsson M, Parkinson J, Rodgers RP, Rogers S, Schmitt-Kopplin P, Schoenmakers PJ, Shintu L, Soong R, Summerfield S, Surman A, Uhrín D, van der Hooft JJJ. Future challenges and new approaches: general discussion. Faraday Discuss. 2019 Aug 15;218:505-523. doi: 10.1039/c9fd90046b. PMID: 31380556.
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Data mining and visualisation: general discussion.

Faraday discussions

Afonso C, Barrow MP, Davies AN, Delsuc MA, Ebbels T, Fernandez-Lima F, Gauchotte-Lindsay C, Giusti P, Goodacre R, Hertkorn N, Jansen JJ, Jones D, Kew W, Kuhn S, Le Guennec A, Lubben A, Parkinson J, Paša-Tolić L, Rogers S, Rudd TR, Schoenmakers PJ, Sidebottom P, Summerfield S, Tinnevelt GH, Trifirò G, Trygg J, van der Hooft JJJ.
PMID: 31373341
Faraday Discuss. 2019 Aug 15;218:354-371. doi: 10.1039/C9FD90044F.

No abstract available.

Cite
Afonso C, Barrow MP, Davies AN, et al. Data mining and visualisation: general discussion. Faraday Discuss. 2019;218:354-371doi: 10.1039/C9FD90044F.
Afonso, C., Barrow, M. P., Davies, A. N., Delsuc, M. A., Ebbels, T., Fernandez-Lima, F., Gauchotte-Lindsay, C., Giusti, P., Goodacre, R., Hertkorn, N., Jansen, J. J., Jones, D., Kew, W., Kuhn, S., Le Guennec, A., Lubben, A., Parkinson, J., Paša-Tolić, L., Rogers, S., Rudd, T. R., Schoenmakers, P. J., Sidebottom, P., Summerfield, S., Tinnevelt, G. H., Trifirò, G., Trygg, J., & van der Hooft, J. J. J. (2019). Data mining and visualisation: general discussion. Faraday discussions, 218354-371. https://doi.org/10.1039/C9FD90044F
Afonso, Carlos, et al. "Data mining and visualisation: general discussion." Faraday discussions vol. 218 (2019): 354-371. doi: https://doi.org/10.1039/C9FD90044F
Afonso C, Barrow MP, Davies AN, Delsuc MA, Ebbels T, Fernandez-Lima F, Gauchotte-Lindsay C, Giusti P, Goodacre R, Hertkorn N, Jansen JJ, Jones D, Kew W, Kuhn S, Le Guennec A, Lubben A, Parkinson J, Paša-Tolić L, Rogers S, Rudd TR, Schoenmakers PJ, Sidebottom P, Summerfield S, Tinnevelt GH, Trifirò G, Trygg J, van der Hooft JJJ. Data mining and visualisation: general discussion. Faraday Discuss. 2019 Aug 15;218:354-371. doi: 10.1039/C9FD90044F. PMID: 31373341.
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Problems of adults with a mitochondrial disease - the patients' perspective: focus on loss.

JIMD reports

Noorda G, van Achterberg T, van der Hooft T, Smeitink J, Schoonhoven L, van Engelen B.
PMID: 23430944
JIMD Rep. 2012;6:85-94. doi: 10.1007/8904_2011_121. Epub 2012 Feb 24.

OBJECTIVE: This study aimed to identify problems as experienced by adults with a mitochondrial disease. We chose to describe these problems from the patients' perspective as we thought this would give optimal input for care improvement.DESIGN: A qualitative design...

Cite
Noorda G, van Achterberg T, van der Hooft T, et al. Problems of adults with a mitochondrial disease - the patients' perspective: focus on loss. JIMD Rep. 2012;6:85-94doi: 10.1007/8904_2011_121.
Noorda, G., van Achterberg, T., van der Hooft, T., Smeitink, J., Schoonhoven, L., & van Engelen, B. (2012). Problems of adults with a mitochondrial disease - the patients' perspective: focus on loss. JIMD reports, 685-94. https://doi.org/10.1007/8904_2011_121
Noorda, Greet, et al. "Problems of adults with a mitochondrial disease - the patients' perspective: focus on loss." JIMD reports vol. 6 (2012): 85-94. doi: https://doi.org/10.1007/8904_2011_121
Noorda G, van Achterberg T, van der Hooft T, Smeitink J, Schoonhoven L, van Engelen B. Problems of adults with a mitochondrial disease - the patients' perspective: focus on loss. JIMD Rep. 2012;6:85-94. doi: 10.1007/8904_2011_121. Epub 2012 Feb 24. PMID: 23430944; PMCID: PMC3565683.
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Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

Mass spectrometry (Tokyo, Japan)

Ridder L, van der Hooft JJ, Verhoeven S.
PMID: 26819876
Mass Spectrom (Tokyo). 2014;3:S0033. doi: 10.5702/massspectrometry.S0033. Epub 2014 Jul 02.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular...

Cite
Ridder L, van der Hooft JJ, Verhoeven S. Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa. Mass Spectrom (Tokyo). 2014;3:S0033doi: 10.5702/massspectrometry.S0033.
Ridder, L., van der Hooft, J. J., & Verhoeven, S. (2014). Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa. Mass spectrometry (Tokyo, Japan), 3S0033. https://doi.org/10.5702/massspectrometry.S0033
Ridder, Lars, et al. "Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa." Mass spectrometry (Tokyo, Japan) vol. 3 (2014): S0033. doi: https://doi.org/10.5702/massspectrometry.S0033
Ridder L, van der Hooft JJ, Verhoeven S. Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa. Mass Spectrom (Tokyo). 2014;3:S0033. doi: 10.5702/massspectrometry.S0033. Epub 2014 Jul 02. PMID: 26819876; PMCID: PMC4321337.
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A community resource for paired genomic and metabolomic data mining.

Nature chemical biology

Schorn MA, Verhoeven S, Ridder L, Huber F, Acharya DD, Aksenov AA, Aleti G, Moghaddam JA, Aron AT, Aziz S, Bauermeister A, Bauman KD, Baunach M, Beemelmanns C, Beman JM, Berlanga-Clavero MV, Blacutt AA, Bode HB, Boullie A, Brejnrod A, Bugni TS, Calteau A, Cao L, Carrión VJ, Castelo-Branco R, Chanana S, Chase AB, Chevrette MG, Costa-Lotufo LV, Crawford JM, Currie CR, Cuypers B, Dang T, de Rond T, Demko AM, Dittmann E, Du C, Drozd C, Dujardin JC, Dutton RJ, Edlund A, Fewer DP, Garg N, Gauglitz JM, Gentry EC, Gerwick L, Glukhov E, Gross H, Gugger M, Guillén Matus DG, Helfrich EJN, Hempel BF, Hur JS, Iorio M, Jensen PR, Kang KB, Kaysser L, Kelleher NL, Kim CS, Kim KH, Koester I, König GM, Leao T, Lee SR, Lee YY, Li X, Little JC, Maloney KN, Männle D, Martin H C, McAvoy AC, Metcalf WW, Mohimani H, Molina-Santiago C, Moore BS, Mullowney MW, Muskat M, Nothias LF, O'Neill EC, Parkinson EI, Petras D, Piel J, Pierce EC, Pires K, Reher R, Romero D, Roper MC, Rust M, Saad H, Saenz C, Sanchez LM, Sørensen SJ, Sosio M, Süssmuth RD, Sweeney D, Tahlan K, Thomson RJ, Tobias NJ, Trindade-Silva AE, van Wezel GP, Wang M, Weldon KC, Zhang F, Ziemert N, Duncan KR, Crüsemann M, Rogers S, Dorrestein PC, Medema MH, van der Hooft JJJ.
PMID: 33589842
Nat Chem Biol. 2021 Apr;17(4):363-368. doi: 10.1038/s41589-020-00724-z.

No abstract available.

Cite
Schorn MA, Verhoeven S, Ridder L, et al. A community resource for paired genomic and metabolomic data mining. Nat Chem Biol. 2021;17(4):363-368doi: 10.1038/s41589-020-00724-z.
Schorn, M. A., Verhoeven, S., Ridder, L., Huber, F., Acharya, D. D., Aksenov, A. A., Aleti, G., Moghaddam, J. A., Aron, A. T., Aziz, S., Bauermeister, A., Bauman, K. D., Baunach, M., Beemelmanns, C., Beman, J. M., Berlanga-Clavero, M. V., Blacutt, A. A., Bode, H. B., Boullie, A., Brejnrod, A., Bugni, T. S., Calteau, A., Cao, L., Carrión, V. J., Castelo-Branco, R., Chanana, S., Chase, A. B., Chevrette, M. G., Costa-Lotufo, L. V., Crawford, J. M., Currie, C. R., Cuypers, B., Dang, T., de Rond, T., Demko, A. M., Dittmann, E., Du, C., Drozd, C., Dujardin, J. C., Dutton, R. J., Edlund, A., Fewer, D. P., Garg, N., Gauglitz, J. M., Gentry, E. C., Gerwick, L., Glukhov, E., Gross, H., Gugger, M., Guillén Matus, D. G., Helfrich, E. J. N., Hempel, B. F., Hur, J. S., Iorio, M., Jensen, P. R., Kang, K. B., Kaysser, L., Kelleher, N. L., Kim, C. S., Kim, K. H., Koester, I., König, G. M., Leao, T., Lee, S. R., Lee, Y. Y., Li, X., Little, J. C., Maloney, K. N., Männle, D., Martin H, C., McAvoy, A. C., Metcalf, W. W., Mohimani, H., Molina-Santiago, C., Moore, B. S., Mullowney, M. W., Muskat, M., Nothias, L. F., O'Neill, E. C., Parkinson, E. I., Petras, D., Piel, J., Pierce, E. C., Pires, K., Reher, R., Romero, D., Roper, M. C., Rust, M., Saad, H., Saenz, C., Sanchez, L. M., Sørensen, S. J., Sosio, M., Süssmuth, R. D., Sweeney, D., Tahlan, K., Thomson, R. J., Tobias, N. J., Trindade-Silva, A. E., van Wezel, G. P., Wang, M., Weldon, K. C., Zhang, F., Ziemert, N., Duncan, K. R., Crüsemann, M., Rogers, S., Dorrestein, P. C., Medema, M. H., & van der Hooft, J. J. J. (2021). A community resource for paired genomic and metabolomic data mining. Nature chemical biology, 17(4), 363-368. https://doi.org/10.1038/s41589-020-00724-z
Schorn, Michelle A, et al. "A community resource for paired genomic and metabolomic data mining." Nature chemical biology vol. 17,4 (2021): 363-368. doi: https://doi.org/10.1038/s41589-020-00724-z
Schorn MA, Verhoeven S, Ridder L, Huber F, Acharya DD, Aksenov AA, Aleti G, Moghaddam JA, Aron AT, Aziz S, Bauermeister A, Bauman KD, Baunach M, Beemelmanns C, Beman JM, Berlanga-Clavero MV, Blacutt AA, Bode HB, Boullie A, Brejnrod A, Bugni TS, Calteau A, Cao L, Carrión VJ, Castelo-Branco R, Chanana S, Chase AB, Chevrette MG, Costa-Lotufo LV, Crawford JM, Currie CR, Cuypers B, Dang T, de Rond T, Demko AM, Dittmann E, Du C, Drozd C, Dujardin JC, Dutton RJ, Edlund A, Fewer DP, Garg N, Gauglitz JM, Gentry EC, Gerwick L, Glukhov E, Gross H, Gugger M, Guillén Matus DG, Helfrich EJN, Hempel BF, Hur JS, Iorio M, Jensen PR, Kang KB, Kaysser L, Kelleher NL, Kim CS, Kim KH, Koester I, König GM, Leao T, Lee SR, Lee YY, Li X, Little JC, Maloney KN, Männle D, Martin H C, McAvoy AC, Metcalf WW, Mohimani H, Molina-Santiago C, Moore BS, Mullowney MW, Muskat M, Nothias LF, O'Neill EC, Parkinson EI, Petras D, Piel J, Pierce EC, Pires K, Reher R, Romero D, Roper MC, Rust M, Saad H, Saenz C, Sanchez LM, Sørensen SJ, Sosio M, Süssmuth RD, Sweeney D, Tahlan K, Thomson RJ, Tobias NJ, Trindade-Silva AE, van Wezel GP, Wang M, Weldon KC, Zhang F, Ziemert N, Duncan KR, Crüsemann M, Rogers S, Dorrestein PC, Medema MH, van der Hooft JJJ. A community resource for paired genomic and metabolomic data mining. Nat Chem Biol. 2021 Apr;17(4):363-368. doi: 10.1038/s41589-020-00724-z. PMID: 33589842; PMCID: PMC7987574.
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BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes.

mSystems

Pascal Andreu V, Augustijn HE, van den Berg K, van der Hooft JJJ, Fischbach MA, Medema MH.
PMID: 34581602
mSystems. 2021 Oct 26;6(5):e0093721. doi: 10.1128/mSystems.00937-21. Epub 2021 Sep 28.

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of these metabolic...

Cite
Pascal Andreu V, Augustijn HE, van den Berg K, et al. BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes. mSystems. 2021;6(5):e0093721doi: 10.1128/mSystems.00937-21.
Pascal Andreu, V., Augustijn, H. E., van den Berg, K., van der Hooft, J. J. J., Fischbach, M. A., & Medema, M. H. (2021). BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes. mSystems, 6(5), e0093721. https://doi.org/10.1128/mSystems.00937-21
Pascal Andreu, Victória, et al. "BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes." mSystems vol. 6,5 (2021): e0093721. doi: https://doi.org/10.1128/mSystems.00937-21
Pascal Andreu V, Augustijn HE, van den Berg K, van der Hooft JJJ, Fischbach MA, Medema MH. BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes. mSystems. 2021 Oct 26;6(5):e0093721. doi: 10.1128/mSystems.00937-21. Epub 2021 Sep 28. PMID: 34581602; PMCID: PMC8547482.
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Ranking Metabolite Sets by Their Activity Levels.

Metabolites

McLuskey K, Wandy J, Vincent I, van der Hooft JJJ, Rogers S, Burgess K, Daly R.
PMID: 33670102
Metabolites. 2021 Feb 11;11(2). doi: 10.3390/metabo11020103.

Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in...

Cite
McLuskey K, Wandy J, Vincent I, et al. Ranking Metabolite Sets by Their Activity Levels. Metabolites. 2021;11(2)doi: 10.3390/metabo11020103.
McLuskey, K., Wandy, J., Vincent, I., van der Hooft, J. J. J., Rogers, S., Burgess, K., & Daly, R. (2021). Ranking Metabolite Sets by Their Activity Levels. Metabolites, 11(2), . https://doi.org/10.3390/metabo11020103
McLuskey, Karen, et al. "Ranking Metabolite Sets by Their Activity Levels." Metabolites vol. 11,2 (2021). doi: https://doi.org/10.3390/metabo11020103
McLuskey K, Wandy J, Vincent I, van der Hooft JJJ, Rogers S, Burgess K, Daly R. Ranking Metabolite Sets by Their Activity Levels. Metabolites. 2021 Feb 11;11(2). doi: 10.3390/metabo11020103. PMID: 33670102; PMCID: PMC7916825.
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