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EPIC-DB: a proteomics database for studying Apicomplexan organisms

BACKGROUND: High throughput proteomics experiments are useful for analyzing the protein expression of an organism, identifying the correct gene structure of a genome, or locating possible post-translational modifications within proteins. High throughput methods necessitate publicly accessible and ea...

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Autores principales: Madrid-Aliste, Carlos J, Dybas, Joseph M, Angeletti, Ruth Hogue, Weiss, Louis M, Kim, Kami, Simon, Istvan, Fiser, Andras
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652494/
https://www.ncbi.nlm.nih.gov/pubmed/19159464
http://dx.doi.org/10.1186/1471-2164-10-38
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author Madrid-Aliste, Carlos J
Dybas, Joseph M
Angeletti, Ruth Hogue
Weiss, Louis M
Kim, Kami
Simon, Istvan
Fiser, Andras
author_facet Madrid-Aliste, Carlos J
Dybas, Joseph M
Angeletti, Ruth Hogue
Weiss, Louis M
Kim, Kami
Simon, Istvan
Fiser, Andras
author_sort Madrid-Aliste, Carlos J
collection PubMed
description BACKGROUND: High throughput proteomics experiments are useful for analyzing the protein expression of an organism, identifying the correct gene structure of a genome, or locating possible post-translational modifications within proteins. High throughput methods necessitate publicly accessible and easily queried databases for efficiently and logically storing, displaying, and analyzing the large volume of data. DESCRIPTION: EPICDB is a publicly accessible, queryable, relational database that organizes and displays experimental, high throughput proteomics data for Toxoplasma gondii and Cryptosporidium parvum. Along with detailed information on mass spectrometry experiments, the database also provides antibody experimental results and analysis of functional annotations, comparative genomics, and aligned expressed sequence tag (EST) and genomic open reading frame (ORF) sequences. The database contains all available alternative gene datasets for each organism, which comprises a complete theoretical proteome for the respective organism, and all data is referenced to these sequences. The database is structured around clusters of protein sequences, which allows for the evaluation of redundancy, protein prediction discrepancies, and possible splice variants. The database can be expanded to include genomes of other organisms for which proteome-wide experimental data are available. CONCLUSION: EPICDB is a comprehensive database of genome-wide T. gondii and C. parvum proteomics data and incorporates many features that allow for the analysis of the entire proteomes and/or annotation of specific protein sequences. EPICDB is complementary to other -genomics- databases of these organisms by offering complete mass spectrometry analysis on a comprehensive set of all available protein sequences.
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spelling pubmed-26524942009-03-07 EPIC-DB: a proteomics database for studying Apicomplexan organisms Madrid-Aliste, Carlos J Dybas, Joseph M Angeletti, Ruth Hogue Weiss, Louis M Kim, Kami Simon, Istvan Fiser, Andras BMC Genomics Database BACKGROUND: High throughput proteomics experiments are useful for analyzing the protein expression of an organism, identifying the correct gene structure of a genome, or locating possible post-translational modifications within proteins. High throughput methods necessitate publicly accessible and easily queried databases for efficiently and logically storing, displaying, and analyzing the large volume of data. DESCRIPTION: EPICDB is a publicly accessible, queryable, relational database that organizes and displays experimental, high throughput proteomics data for Toxoplasma gondii and Cryptosporidium parvum. Along with detailed information on mass spectrometry experiments, the database also provides antibody experimental results and analysis of functional annotations, comparative genomics, and aligned expressed sequence tag (EST) and genomic open reading frame (ORF) sequences. The database contains all available alternative gene datasets for each organism, which comprises a complete theoretical proteome for the respective organism, and all data is referenced to these sequences. The database is structured around clusters of protein sequences, which allows for the evaluation of redundancy, protein prediction discrepancies, and possible splice variants. The database can be expanded to include genomes of other organisms for which proteome-wide experimental data are available. CONCLUSION: EPICDB is a comprehensive database of genome-wide T. gondii and C. parvum proteomics data and incorporates many features that allow for the analysis of the entire proteomes and/or annotation of specific protein sequences. EPICDB is complementary to other -genomics- databases of these organisms by offering complete mass spectrometry analysis on a comprehensive set of all available protein sequences. BioMed Central 2009-01-21 /pmc/articles/PMC2652494/ /pubmed/19159464 http://dx.doi.org/10.1186/1471-2164-10-38 Text en Copyright © 2009 Madrid-Aliste 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 Database
Madrid-Aliste, Carlos J
Dybas, Joseph M
Angeletti, Ruth Hogue
Weiss, Louis M
Kim, Kami
Simon, Istvan
Fiser, Andras
EPIC-DB: a proteomics database for studying Apicomplexan organisms
title EPIC-DB: a proteomics database for studying Apicomplexan organisms
title_full EPIC-DB: a proteomics database for studying Apicomplexan organisms
title_fullStr EPIC-DB: a proteomics database for studying Apicomplexan organisms
title_full_unstemmed EPIC-DB: a proteomics database for studying Apicomplexan organisms
title_short EPIC-DB: a proteomics database for studying Apicomplexan organisms
title_sort epic-db: a proteomics database for studying apicomplexan organisms
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652494/
https://www.ncbi.nlm.nih.gov/pubmed/19159464
http://dx.doi.org/10.1186/1471-2164-10-38
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