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Comprised of 6,426 sample runs, The Environmental Determinants of Diabetes in the Young (TEDDY) proteomics validation study constitutes one of the largest targeted proteomics studies in the literature to date. Making quality control (QC) and donor sample data available to researchers aligns with...

HDF5 file containing 10,000 hydraulic transmissivity inputs and the corresponding hydraulic pressure field outputs for a two-dimensional saturated flow model of the Hanford Site. The inputs are generated by sampling a 1,000-dimensional Kosambi-Karhunen-Loève (KKL) model of the transmissivity field...

Inclusion levels of alternative splicing (AS) events of five different varieties (i.e. skipped exon (SE), retained intron (RI), alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS), and mutually exclusive exons (MXE)) were measured in human blood samples from two separate cohorts of...

Please cite as : McClure R.S., Y. Farris, R.E. Danczak, W.C. Nelson, H. Song, A. Kessler, and J. Lee, et al. 2022. Model Soil Consortium 2 (MSC-2) Bacterial Isolate Genomes. [Data Set] PNNL DataHub. https://doi.org/10.25584/PNNLDH/1986536 Model Soil Consortium 2 (MSC-2) Bacterial Isolate Genomes...

Last updated on 2023-05-02T18:08:23+00:00 by LN Anderson Fungal Monoisolate Multi-Omics Data Package DOI "KS4A-Omics1.0_FspDS68" Molecular mechanisms underlying fungal mineral weathering and nutrient translocation in low nutrient environments remain poorly resolved, due to the lack of a platform for...

Please cite as : Anderson L.N., J.E. McDermott, and R.S. McClure. 2020. WA-IsoC_MSC1.1.0 (Amplicon 16S rRNA, WA). [Data Set] PNNL DataHub. https://doi.org/10.25584/WAIsoCMSC1/1635272 The soil microbiome is central to the cycling of carbon and other nutrients and to the promotion of plant growth...

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. KS-TmG.1.0 (Metagenome, KS). [Data Set] PNNL DataHub. https://doi.org/10.25584/KSTmG1/1635004 To enable a comprehensive survey of the metabolic potential of complex soil...
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. IA-TmG.1.0 (Metagenome, IA). [Data Set] PNNL DataHub. https://doi.org/10.25584/IATmG1/1635005 To enable a comprehensive survey of the metabolic potential of complex soil...

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...

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery...

Metabolite exchange between plant roots and their associated rhizosphere microbiomes underpins plant growth promotion by microbes. Sorghum bicolor is a cereal crop that feeds animals and humans and is used for bioethanol production. Its root tips exude large amounts of a lipophilic benzoquinone...

The Sequel II System Sequencer is a high-throughput DNA sequencer machine developed and manufactured by PacBio , and is designed for high throughput, production-scale sequencing laboratories. Originally released in 2015, the Sequel system provides Single Molecule, Real-Time (SMRT) sequencing core...

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...

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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...

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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...

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