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AutoFACT: An Automatic Functional Annotation and Classification Tool

BACKGROUND: Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. RESULTS: We present AutoFACT, a fully automated and cu...

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Autores principales: Koski, Liisa B, Gray, Michael W, Lang, B Franz, Burger, Gertraud
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182349/
https://www.ncbi.nlm.nih.gov/pubmed/15960857
http://dx.doi.org/10.1186/1471-2105-6-151
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author Koski, Liisa B
Gray, Michael W
Lang, B Franz
Burger, Gertraud
author_facet Koski, Liisa B
Gray, Michael W
Lang, B Franz
Burger, Gertraud
author_sort Koski, Liisa B
collection PubMed
description BACKGROUND: Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. RESULTS: We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. CONCLUSION: AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at .
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spelling pubmed-11823492005-08-04 AutoFACT: An Automatic Functional Annotation and Classification Tool Koski, Liisa B Gray, Michael W Lang, B Franz Burger, Gertraud BMC Bioinformatics Software BACKGROUND: Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. RESULTS: We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1–2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. CONCLUSION: AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at . BioMed Central 2005-06-16 /pmc/articles/PMC1182349/ /pubmed/15960857 http://dx.doi.org/10.1186/1471-2105-6-151 Text en Copyright © 2005 Koski 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
Koski, Liisa B
Gray, Michael W
Lang, B Franz
Burger, Gertraud
AutoFACT: An Automatic Functional Annotation and Classification Tool
title AutoFACT: An Automatic Functional Annotation and Classification Tool
title_full AutoFACT: An Automatic Functional Annotation and Classification Tool
title_fullStr AutoFACT: An Automatic Functional Annotation and Classification Tool
title_full_unstemmed AutoFACT: An Automatic Functional Annotation and Classification Tool
title_short AutoFACT: An Automatic Functional Annotation and Classification Tool
title_sort autofact: an automatic functional annotation and classification tool
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182349/
https://www.ncbi.nlm.nih.gov/pubmed/15960857
http://dx.doi.org/10.1186/1471-2105-6-151
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