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High-throughput functional annotation and data mining with the Blast2GO suite
Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene product...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2425479/ https://www.ncbi.nlm.nih.gov/pubmed/18445632 http://dx.doi.org/10.1093/nar/gkn176 |
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author | Götz, Stefan García-Gómez, Juan Miguel Terol, Javier Williams, Tim D. Nagaraj, Shivashankar H. Nueda, María José Robles, Montserrat Talón, Manuel Dopazo, Joaquín Conesa, Ana |
author_facet | Götz, Stefan García-Gómez, Juan Miguel Terol, Javier Williams, Tim D. Nagaraj, Shivashankar H. Nueda, María José Robles, Montserrat Talón, Manuel Dopazo, Joaquín Conesa, Ana |
author_sort | Götz, Stefan |
collection | PubMed |
description | Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data. |
format | Text |
id | pubmed-2425479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-24254792008-06-12 High-throughput functional annotation and data mining with the Blast2GO suite Götz, Stefan García-Gómez, Juan Miguel Terol, Javier Williams, Tim D. Nagaraj, Shivashankar H. Nueda, María José Robles, Montserrat Talón, Manuel Dopazo, Joaquín Conesa, Ana Nucleic Acids Res Computational Biology Functional genomics technologies have been widely adopted in the biological research of both model and non-model species. An efficient functional annotation of DNA or protein sequences is a major requirement for the successful application of these approaches as functional information on gene products is often the key to the interpretation of experimental results. Therefore, there is an increasing need for bioinformatics resources which are able to cope with large amount of sequence data, produce valuable annotation results and are easily accessible to laboratories where functional genomics projects are being undertaken. We present the Blast2GO suite as an integrated and biologist-oriented solution for the high-throughput and automatic functional annotation of DNA or protein sequences based on the Gene Ontology vocabulary. The most outstanding Blast2GO features are: (i) the combination of various annotation strategies and tools controlling type and intensity of annotation, (ii) the numerous graphical features such as the interactive GO-graph visualization for gene-set function profiling or descriptive charts, (iii) the general sequence management features and (iv) high-throughput capabilities. We used the Blast2GO framework to carry out a detailed analysis of annotation behaviour through homology transfer and its impact in functional genomics research. Our aim is to offer biologists useful information to take into account when addressing the task of functionally characterizing their sequence data. Oxford University Press 2008-06 2008-04-29 /pmc/articles/PMC2425479/ /pubmed/18445632 http://dx.doi.org/10.1093/nar/gkn176 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Götz, Stefan García-Gómez, Juan Miguel Terol, Javier Williams, Tim D. Nagaraj, Shivashankar H. Nueda, María José Robles, Montserrat Talón, Manuel Dopazo, Joaquín Conesa, Ana High-throughput functional annotation and data mining with the Blast2GO suite |
title | High-throughput functional annotation and data mining with the Blast2GO suite |
title_full | High-throughput functional annotation and data mining with the Blast2GO suite |
title_fullStr | High-throughput functional annotation and data mining with the Blast2GO suite |
title_full_unstemmed | High-throughput functional annotation and data mining with the Blast2GO suite |
title_short | High-throughput functional annotation and data mining with the Blast2GO suite |
title_sort | high-throughput functional annotation and data mining with the blast2go suite |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2425479/ https://www.ncbi.nlm.nih.gov/pubmed/18445632 http://dx.doi.org/10.1093/nar/gkn176 |
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