Database
EPIC-DB: a proteomics database for studying Apicomplexan organisms
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* Corresponding author: Andras Fiser afiser@aecom.yu.edu
1 Biodefense Proteomics Research Center, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
2 Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
3 Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
4 Department of Pathology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
5 Department of Developmental and Molecular Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
6 Department of Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
7 Department of Microbiology and Immunology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
8 Laboratory for Macromolecular Analysis and Proteomics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
9 Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
10 Institute of Enzymology, Hungarian Academy of Sciences, Karolina ut 29, Budapest 1113, Hungary
BMC Genomics 2009, 10:38 doi:10.1186/1471-2164-10-38
Published: 21 January 2009Abstract
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.