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A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome
View ORCID ProfileAnnamarie Bustion, View ORCID ProfileAyushi Agrawal, View ORCID ProfilePeter J. Turnbaugh, View ORCID ProfileKatherine S. Pollard
doi: https://doi.org/10.1101/2022.08.02.502504
Annamarie Bustion
1Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, CA, USA
2Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
Ayushi Agrawal
2Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
Peter J. Turnbaugh
3Department of Microbiology & Immunology, University of California San Francisco, San Francisco, CA 94143, USA
4Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
Katherine S. Pollard
2Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA 94158, USA
4Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
5Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
6Institute for Human Genetics, University of California, San Francisco, CA 94143, USA
7Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA
Posted August 11, 2022.
A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome
Annamarie Bustion, Ayushi Agrawal, Peter J. Turnbaugh, Katherine S. Pollard
bioRxiv 2022.08.02.502504; doi: https://doi.org/10.1101/2022.08.02.502504
A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome
Annamarie Bustion, Ayushi Agrawal, Peter J. Turnbaugh, Katherine S. Pollard
bioRxiv 2022.08.02.502504; doi: https://doi.org/10.1101/2022.08.02.502504
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