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Arkin wins DOE's Ernest Orlando Lawrence Award

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Posted by nlharris under News
Adam Arkin, KBase's Principal Investigator and the director of Berkeley Lab’s Physical Biosciences Division (PBD), has been awarded the 2013 Ernest Orlando Lawrence Award, the Department of Energy (DOE)’s highest scientific honor, in recognition “for his work advancing biological and environmental sciences.”
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Scheduled maintenance on March 25, 2014

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Posted by nlharris
KBase will be undergoing maintenance on Tuesday, March 25, 2014, from 8am-12pm PDT. There may be intermittent disruptions in services during this time.
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Adam Arkin to speak about KBase at DOE JGI User Meeting

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Posted by nlharris under News
The annual DOE JGI User Meeting, "Genomics of Energy & Environment" is underway right now in Walnut Creek, CA. KBase's lead PI, Adam Arkin, will speak at the meeting tomorrow (March 20) at 1:45pm. Dr. Arkin will describe KBase's open-source, open-architecture framework for reproducible and collaborative computational systems biology, including the new Narrative user interface, which provides a transparent, reproducible, and persistent view of the computational steps and thought processes leading to a particular conclusion or hypothesis.
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The Department of Energy Systems Biology Knowledgebase (KBase)  is an emerging software and data environment designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses on a scalable computing infrastructure, and model interactions in microbes, plants, and their communities. KBase provides an open, extensible framework for secure sharing of data, tools, and scientific conclusions in predictive and systems biology.

 KBase includes

  • 925 data types
  • 5695 bacterial/archaeal genomes
  • 175 eukaryotic genomes
  • 4985 models
  • 23 services
  • 821 functions

 What can KBase do?

  • Efficiently annotate new microbial genomes and infer metabolic and regulatory networks.
  • Transform network inferences into metabolic models and map missing reactions to genes using novel data reconciliation tools.
  • Test microbial ecological hypotheses through taxonomic and functional analysis of quality-assessed metagenomic data
  • Discover genetic variations within plant populations and map these to complex organismal traits.