Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.

Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.

This week at AAAS, PNNL scientists discuss phenomics and the factors that influence DNA instructions to bring about the world around us.

Scientists plumbed the depths of nearly 3,000 soil samples from around the globe to compile the heftiest atlas of soil viruses ever created.

The Pacific Northwest Association of Toxicologists (PANWAT) presented its annual Toxicology Achievement Award to Katrina Waters at the Society of Toxicology Pacific Northwest Chapter Meeting, held in Lynnwood, Washington, on September 30th.

Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.

Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.

A compilation of soil viral genomes provides a comprehensive description of the soil virosphere, its potential to impact global biogeochemistry, and an open database for future investigations of soil viral ecology.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

Scientists have learned more about how drug resistance develops in patients with acute myeloid leukemia and how the process might be slowed.

The new PTM-Psi workflow opens the door for uncovering molecular insights within proteomics datasets.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The virus that causes COVID-19 takes over the body’s fat-processing system and boosts cellular triglycerides as it causes disease.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.