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FANTOM: Functional and taxonomic analysis of metagenomes

BACKGROUND: Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional...

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Detalles Bibliográficos
Autores principales: Sanli, Kemal, Karlsson, Fredrik H, Nookaew, Intawat, Nielsen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566963/
https://www.ncbi.nlm.nih.gov/pubmed/23375020
http://dx.doi.org/10.1186/1471-2105-14-38
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author Sanli, Kemal
Karlsson, Fredrik H
Nookaew, Intawat
Nielsen, Jens
author_facet Sanli, Kemal
Karlsson, Fredrik H
Nookaew, Intawat
Nielsen, Jens
author_sort Sanli, Kemal
collection PubMed
description BACKGROUND: Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. RESULTS: Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases. CONCLUSIONS: The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom.
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spelling pubmed-35669632013-02-11 FANTOM: Functional and taxonomic analysis of metagenomes Sanli, Kemal Karlsson, Fredrik H Nookaew, Intawat Nielsen, Jens BMC Bioinformatics Software BACKGROUND: Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. RESULTS: Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases. CONCLUSIONS: The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom. BioMed Central 2013-02-01 /pmc/articles/PMC3566963/ /pubmed/23375020 http://dx.doi.org/10.1186/1471-2105-14-38 Text en Copyright ©2013 Sanli et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
Sanli, Kemal
Karlsson, Fredrik H
Nookaew, Intawat
Nielsen, Jens
FANTOM: Functional and taxonomic analysis of metagenomes
title FANTOM: Functional and taxonomic analysis of metagenomes
title_full FANTOM: Functional and taxonomic analysis of metagenomes
title_fullStr FANTOM: Functional and taxonomic analysis of metagenomes
title_full_unstemmed FANTOM: Functional and taxonomic analysis of metagenomes
title_short FANTOM: Functional and taxonomic analysis of metagenomes
title_sort fantom: functional and taxonomic analysis of metagenomes
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566963/
https://www.ncbi.nlm.nih.gov/pubmed/23375020
http://dx.doi.org/10.1186/1471-2105-14-38
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