R24 Funded Resources - Bioanalytical Tools

Biomedical National Elemental Imaging Resource (BNEIR)
Dartmouth College

R24 Grant Number: R24GM141194
Principal Investigator: Brian P. Jackson
The Biomedical National Elemental Imaging Resource (BNEIR), which will accelerate and simplify access for biomedical researchers to instrumentation, expertise, web-based and in-person training, after-visit support, software and will foster a dynamic community for elemental imaging users.

IDeA National Resource for Quantitative Proteomics
University of Arkansas for Medical Sciences

R24 Grant Number: R24GM137786
Principal Investigator: Alan Tackett
The IDeA National Resource for Quantitative Proteomics was created to: 1) Provide state-of-the-art quantitative proteomics services to the IDeA network, 2) Provide outreach opportunities for quantitative proteomics to the IDeA network, and 3) Provide educational opportunities for quantitative proteomics to the IDeA network. This Resource was previously supported by P20GM103429 and P20GM121293.

National Center for Functional Glycomics (NCFG)
Harvard Medical School

R24 Grant Number: R24GM137763
Principal Investigator: Richard D. Cummings
The National Center for Functional Glycomics aims to enable researchers in the under-served area of glycosciences and whose research would benefit from glycoscience tools and expertise to provide continued and improved access to state-of-the-art technologies to advance biomedical research and human health involving protein-glycan interactions and glycan recognition. This Center was previously supported by the BTRR Program (P41GM103694).

​National Glycoscience Resource-CCRC Service and Training
University of Georgia

R24 Grant Number: R24GM137782
Principal Investigator: Parastoo Azadi
The National Resource in Glycoscience that will offer service and hands-on training for compositional analysis and detailed structural characterization of all classes of glycoconjugates, including glycoproteins, polysaccharides, and glycolipids, to the greater scientific community. It was previously supported by the BTRR Program (P41GM103390 and P41GM103490).

National User Resource for Biological Accelerator Mass Spectrometry (AMS)
Lawrence Livermore National Laboratory

R24 Grant Number: R24GM137748
Principal Investigator: Graham Bench
The National User Resource for Biological Accelerator Mass Spectrometry (AMS) provides ultra-high sensitivity, quantitative isotopic analysis for biomedical researchers measuring very low-level radioisotopes (primarily 14C). With over 30 years of expertise in the development and application of AMS for biomedical sciences under the BTRR Program (P41GM103483), the User Resource continues to enhance AMS analysis through utilization of new methods, instrumentation, and training for researchers.

Seattle Quant: A Resource for the Skyline Software Ecosystem
University of Washington

R24 Grant Number: R24GM141156
Principal Investigator: Michael Maccoss
Quantitative mass spectrometry measurements offer a promising alternative to immunological based assays that are the standard for quantitative protein measurements in clinical and basic research laboratories. Critical to these experiments is our software, Skyline and the associated ecosystem of tools, which have been developed to handle the generation of instrument methods and the subsequent analysis of the resulting data. This Resource was previously supported by the BTRR Program (P41GM103533).

The Genomic Enzymology Web-Based Resource
University of Illinois at Urbana-Champaign

R24 Grant Number: R24GM141196
Principal Investigator: John Gerlt
The Genomic Enzymology Web-Based Resource integrates three tools to enable the discovery of novel proteins and metabolic pathways. The tool pipeline is comprised of three analysis steps: (1) generation of sequence similarity networks (SSNs) enabling the semi-automated reconstruction of high-quality protein families built around any protein sequence, (2) parallel exploration of the genome neighborhood of a protein family across a diverse set of input genomes to discover functionally linked gene products to infer novel enzymatic functions and metabolic pathways, and (3) determination of metagenome abundance of clusters in the SSNs to discover important targets for functional assignment. This resource was previously supported by U54GM093342 and P01GM118303.


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