The DOE Systems Biology Knowledgebase (KBase)
Author:
Arkin Adam PORCID, Stevens Rick L, Cottingham Robert W, Maslov Sergei, Henry Christopher S, Dehal Paramvir, Ware Doreen, Perez Fernando, Harris Nomi L, Canon Shane, Sneddon Michael W, Henderson Matthew L, Riehl William J, Gunter Dan, Murphy-Olson Dan, Chan Stephen, Kamimura Roy T, Brettin Thomas S, Meyer Folker, Chivian Dylan, Weston David J, Glass Elizabeth M, Davison Brian H, Kumari Sunita, Allen Benjamin H, Baumohl Jason, Best Aaron A, Bowen Ben, Brenner Steven E, Bun Christopher C, Chandonia John-Marc, Chia Jer-Ming, Colasanti Ric, Conrad Neal, Davis James J, DeJongh Matthew, Devoid Scott, Dietrich Emily, Drake Meghan M, Dubchak Inna, Edirisinghe Janaka N, Fang Gang, Faria José P, Frybarger Paul M, Gerlach Wolfgang, Gerstein Mark, Gurtowski James, Haun Holly L, He Fei, Jain Rashmi, Joachimiak Marcin P, Keegan Kevin P, Kondo Shinnosuke, Kumar Vivek, Land Miriam L, Mills Marissa, Novichkov Pavel, Oh Taeyun, Olsen Gary J, Olson Bob, Parrello Bruce, Pasternak Shiran, Pearson Erik, Poon Sarah S, Price Gavin A, Ramakrishnan Srividya, Ranjan Priya, Ronald Pamela C, Schatz Michael C, Seaver Samuel M D, Shukla Maulik, Sutormin Roman A, Syed Mustafa H, Thomason James, Tintle Nathan L, Wang Daifeng, Xia Fangfang, Yoo Hyunseung, Yoo Shinjae
Abstract
AbstractThe U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology — predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.
Publisher
Cold Spring Harbor Laboratory
Reference55 articles.
1. Ten Simple Rules for the Open Development of Scientific Software 2. Millman, K. J. , and Fernando Pérez. in Implementing reproducible research (ed Friedrich Leisch Victoria Stodden , and Roger D. Peng ) 149–183 (CRC Press, 2014). 3. Enhancing reproducibility for computational methods 4. DOE Systems Biology Knowledgebase Implementation Plan, http://genomicscience.energy.gov/compbio/kbaseplan/index.shtml (2010). 5. IPython: A System for Interactive Scientific Computing
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