Filter results
Category
- (-) Biology (43)
- Scientific Discovery (43)
- Human Health (21)
- Earth System Science (17)
- Microbiome Science (15)
- Integrative Omics (13)
- Computational Research (5)
- Data Analytics & Machine Learning (5)
- Chemistry (1)
- Computational Mathematics & Statistics (1)
- Computational Mathematics & Statistics (1)
- Computing & Analytics (1)
- Data Analytics & Machine Learning (1)
- National Security (1)
- Plant Science (1)
Content type
Tags
- (-) West Nile virus (13)
- (-) PerCon SFA (10)
- (-) High Throughput Sequencing (9)
- (-) Microbiome (8)
- Virology (79)
- Immune Response (53)
- Time Sampled Measurement Datasets (50)
- Gene expression profile data (47)
- Differential Expression Analysis (46)
- Homo sapiens (34)
- Mass spectrometry data (31)
- Multi-Omics (30)
- Viruses (26)
- Omics (24)
- Health (23)
- Virus (23)
- Soil Microbiology (21)
- MERS-CoV (18)
- Mus musculus (18)
- Mass Spectrometry (13)
- sequencing (13)
- Genomics (12)
- Ebola (11)
- Influenza A (11)
- Metagenomics (9)
- Resource Metadata (9)
- Proteomics (8)
- Microarray (7)
- Synthetic Biology (7)
- Imaging (6)
Category
Category
"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...
Category
Elmore JR, Dexter GN, Baldino H, Huenemann JD, Francis R, Peabody GL 5th, Martinez-Baird J, Riley LA, Simmons T, Coleman-Derr D, Guss AM, Egbert RG. High-throughput genetic engineering of nonmodel and undomesticated bacteria via iterative site-specific genome integration. Sci Adv. 2023 Mar 10;9(10)...
The rhizosphere represents a dynamic and complex interface between plant hosts and the microbial community found in the surrounding soil. While it is recognized that manipulating the rhizosphere has the potential to improve plant fitness and health, engineering the rhizosphere microbiome through...
Agriculture is the largest source of greenhouse gases (GHG) production. Conversion of nitrogen fertilizers into more reduced forms by microbes through a process known as biological nitrification drives GHG production, enhances proliferation of toxic algal blooms, and increases cost of crop...
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...
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...
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...
Last updated on 2024-02-11T22:41:43+00:00 by LN Anderson Omics-LHV Profiling of Host Response to West Nile Virus Infection Background West Nile virus ( WNV ) belongs to the mosquito-borne Flaviviridae family and is classified as a Category A priority pathogen by the National Institute of Allergy and...
Category
Datasets
11
Last updated on 2023-02-23T19:37:46+00:00 by LN Anderson PerCon SFA Project Publication Experimental Data Catalog The Persistence Control of Engineered Functions in Complex Soil Microbiomes Project (PerCon SFA) at Pacific Northwest National Laboratory ( PNNL ) is a Genomic Sciences Program...
Datasets
3
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
1
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
1
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
3