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Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from KPBS field site in Manhattan, KS. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from COBS field site in Boone County, IA. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Complete replicate terabase metagenome (TmG.2.0) of grassland soil microbiome collections from IAREC field site in Prosser, WA. Metagenome (unclassified soil sequencing) Data DOI Package, version 2.0.
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Viral communities detected from three large grassland soil metagenomes with historically different precipitation moisture regimes.
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"DNA Viral Diversity, Abundance, and Functional Potential Vary across Grassland Soils with a Range of Historical Moisture Regimes" Soil viruses are abundant, but the influence of the environment and climate on soil viruses remains poorly understood. Here, we addressed this gap by comparing the...
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"Deconstructing the Soil Microbiome into Reduced-Complexity Functional Modules" The soil microbiome represents one of the most complex microbial communities on the planet, encompassing thousands of taxa and metabolic pathways, rendering holistic analyses computationally intensive and difficult. Here...
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Fusarium sp. DS682 Proteogenomics Statistical Data Analysis of SFA dataset download: 10.25584/KSOmicsFspDS682/1766303 . GitHub Repository Source: https://github.com/lmbramer/Fusarium-sp.-DS-682-Proteogenomics MaxQuant Export Files (txt) Trelliscope Boxplots (jsonp) Fusarium Report (.Rmd, html)...
The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...
Datasets
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The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...
Datasets
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Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...
Datasets
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Please cite as : Anderson L.N., W.C. Nelson, J.E. McDermott, R. Wu, S.J. Fansler, Y. Farris, and J.K. Jansson, et al. 2020. WA-TmG.1.0 (Metagenome, WA). [Data Set] PNNL DataHub. https://doi.org/10.25584/WATmG1/1635002 To enable a comprehensive survey of the metabolic potential of complex soil...