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Start Over Author "Duncan, WD" Publisher escholarship, university of california
2 results on '"Duncan, WD"'

1. Leveraging Ontologies within the National Microbiome Data Collaborative

Author

Duncan, WD, Ahmed, F, Anubhav, F, Baumes, J, Beezley, J, Borkum, M, Bramer, L, Canon, S, Chain, P, Christianson, D, Corilo, Y, Davenport, K, Davis, B, Drake, M, Fagnan, K, Flynn, M, Hays, D, Hu, B, Huntemann, M, Kelliher, J, Lebedeva, S, Li, PE, Lipton, M, Lo, CC, Mans, D, Martin, S, McCue, LA, Millard, D, Miller, K, Mouncey, N, Piehowski, P, Jackson, EP, Prymolenna, A, Purvine, S, Reddy, TBK, Richardson, R, Shakya, M, Smith, M, Sundaramurthi, JC, Miller, MA, Unni, D, Vangay, P, Wilson, B, Winston, D, Wood-Charlson, E, Xu, Y, Eloe-Fadrosh, E, and Mungall, CJ

Subjects
Information systems
Abstract

The National Microbiome Data Collaborative (NMDC) is a multi-organizational effort to integrate microbiome data across diverse areas in environmental science. Data provided by the NMDC can then undergo advanced analysis and provide new insights into metagenomics, metatranscriptomics, metaproteomics, and metabolomics. To address these challenges, we have developed our schema using the Linked data Modeling Language (LinkML). This allows us to easily map data to existing standards and ontologies.

Published
2021

2. The National Microbiome Data Collaborative Data Portal: An integrated multi-omics microbiome data resource

Author

Eloe-Fadrosh, EA, Eloe-Fadrosh, EA, Ahmed, F, Anubhav, Babinski, M, Baumes, J, Borkum, M, Bramer, L, Canon, S, Christianson, DS, Corilo, YE, Davenport, KW, Davis, B, Drake, M, Duncan, WD, Flynn, MC, Hays, D, Hu, B, Huntemann, M, Kelliher, J, Lebedeva, S, Li, PE, Lipton, M, Lo, CC, Martin, S, Millard, D, Miller, K, Miller, MA, Piehowski, P, Jackson, EP, Purvine, S, Reddy, TBK, Richardson, R, Rudolph, M, Sarrafan, S, Shakya, M, Smith, M, Stratton, K, Sundaramurthi, JC, Vangay, P, Winston, D, Wood-Charlson, EM, Xu, Y, Chain, PSG, McCue, LA, Mans, D, Mungall, CJ, Mouncey, NJ, Fagnan, K, Eloe-Fadrosh, EA, Eloe-Fadrosh, EA, Ahmed, F, Anubhav, Babinski, M, Baumes, J, Borkum, M, Bramer, L, Canon, S, Christianson, DS, Corilo, YE, Davenport, KW, Davis, B, Drake, M, Duncan, WD, Flynn, MC, Hays, D, Hu, B, Huntemann, M, Kelliher, J, Lebedeva, S, Li, PE, Lipton, M, Lo, CC, Martin, S, Millard, D, Miller, K, Miller, MA, Piehowski, P, Jackson, EP, Purvine, S, Reddy, TBK, Richardson, R, Rudolph, M, Sarrafan, S, Shakya, M, Smith, M, Stratton, K, Sundaramurthi, JC, Vangay, P, Winston, D, Wood-Charlson, EM, Xu, Y, Chain, PSG, McCue, LA, Mans, D, Mungall, CJ, Mouncey, NJ, and Fagnan, K

Abstract

The National Microbiome Data Collaborative (NMDC) Data Portal (https://data.microbiomedata.org) supports microbiome multi-omics data exploration and access through an integrated, distributed data framework aligned with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles (1). The NMDC Data Portal currently hosts 10.2 terabytes of multi-omics microbiome data, spanning five data types (metagenomes, metatranscriptomes, metaproteomes, metabolomes, and natural organic matter characterizations), generated at two Department of Energy User Facilities, the Joint Genome Institute (JGI) at Lawrence Berkeley National Laboratory (LBNL) and the Environmental Molecular Systems Laboratory (EMSL) at Pacific Northwest National Laboratory (PNNL). A flexible data schema (https://github.com/microbiomedata/nmdc-schema) leveraging community-driven standards underpins how data is managed and integrated. Annotated multi-omic data products are produced by the NMDC workflows and linked through common biosamples to enable search capabilities based on environmental context, instrumentation, and functional attributes. As a pilot system, the NMDC Data Portal offers download capabilities and several search components, including interactive geographic visualization of samples; environmental classification distribution visualized through an interactive Sankey diagram; time-series slider to select longitudinal samples of interest; and an upset plot displaying the number of multi-omics data generated from the same biosample within a study.

Published
2022

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