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)...
Filter results
Project Type
Tags
- Virology (14)
- Viruses (10)
- Health (8)
- Soil Microbiology (8)
- Virus (8)
- Omics (6)
- Omics-LHV Project (6)
- Differential Expression Analysis (5)
- Gene expression profile data (5)
- Immune Response (5)
- Multi-Omics (5)
- PerCon SFA (5)
- Time Sampled Measurement Datasets (5)
- Autoimmunity (4)
- Biomarkers (4)
- Homo sapiens (4)
- Ions (4)
- Machine Learning (4)
- Mass Spectrometry (4)
- Mass spectrometry data (4)
- Microbiome (4)
- Molecular Profiling (4)
- Nanoparticles (4)
- sequencing (4)
- Synthetic (4)
- Synthetic Biology (4)
- Type 1 Diabetes (4)
- Fungi (3)
- Genomics (3)
- Spectroscopy (3)
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...
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