The National Microbiome Data Collaborative (NMDC) was founded in 2019 by a diverse group of experts with funding from the Department of Energy to address ongoing data challenges in biology through the creation of new tools and standardized practices. A new article dives into its Ambassador Program and explores how it promotes best practices through community engagement.
A Tool to Find Nomadic Genes that Help Microbes Adapt
Mobile genetic elements (MGEs) are genetic entities that seek to replicate themselves and spread from cell to cell. Two of the most common forms of MGEs are viruses and plasmids. They can be found in virtually all of Earth’s ecosystems. A software tool recently described in Nature Biotechnology called geNomad identifies and classifies MGEs based upon their gene content and their genetic sequences. The software was created by researchers under the direction of JGI Microbiome Data Science Group Lead Nikos Kyrpides.
Integrative Genomics Building Receives DOE Project Management Achievement Award
On October 25, 2021, the Department of Energy recognized the completion of the Integrative Genomics Building (IGB) with a 2020 Project Management Achievement Award. According to the IGB project management award citation, project managers oversaw the completion of the building ahead of schedule and on budget in a highly competitive construction environment.
New Partnership Seeds Microbiome Research
University of California San Francisco (UCSF), UC Davis, and Lawrence Berkeley National Laboratory (Berkeley Lab) have formed a Tri-Institutional Partnership in Microbiome Research (TrIP Microbiome) to catalyze and fund novel, bold, and potentially transformative collaborative microbiome research projects. A unique aspect of the partnership is its data-driven focus and data infrastructure, brought through the participation of the Berkeley Lab-led National Microbiome Data Collaborative (NMDC). The NMDC is working with TrIP Microbiome researchers to catalyze experimental co-design between biologists and computational scientists, adoption of data management best practices, and open science to enable cross-study comparison and machine learning.
Was this page useful?
Send
