BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data

Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the...

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Autores principales: Garcia-Albornoz, Manuel, Thankaswamy-Kosalai, Subazini, Nilsson, Avlant, Väremo, Leif, Nookaew, Intawat, Nielsen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086127/
https://www.ncbi.nlm.nih.gov/pubmed/24792167
http://dx.doi.org/10.1093/nar/gku371
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author Garcia-Albornoz, Manuel
Thankaswamy-Kosalai, Subazini
Nilsson, Avlant
Väremo, Leif
Nookaew, Intawat
Nielsen, Jens
author_facet Garcia-Albornoz, Manuel
Thankaswamy-Kosalai, Subazini
Nilsson, Avlant
Väremo, Leif
Nookaew, Intawat
Nielsen, Jens
author_sort Garcia-Albornoz, Manuel
collection PubMed
description Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.
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spelling pubmed-40861272014-10-28 BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data Garcia-Albornoz, Manuel Thankaswamy-Kosalai, Subazini Nilsson, Avlant Väremo, Leif Nookaew, Intawat Nielsen, Jens Nucleic Acids Res Article Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools. Oxford University Press 2014-07-01 2014-05-03 /pmc/articles/PMC4086127/ /pubmed/24792167 http://dx.doi.org/10.1093/nar/gku371 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Garcia-Albornoz, Manuel
Thankaswamy-Kosalai, Subazini
Nilsson, Avlant
Väremo, Leif
Nookaew, Intawat
Nielsen, Jens
BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title_full BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title_fullStr BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title_full_unstemmed BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title_short BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
title_sort biomet toolbox 2.0: genome-wide analysis of metabolism and omics data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086127/
https://www.ncbi.nlm.nih.gov/pubmed/24792167
http://dx.doi.org/10.1093/nar/gku371
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