MOPED: Model Organism Protein Expression Database

Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43 000 proteins with at least one spectral match and more than 11 million high certainty spectra.

[1]  Eugene Kolker,et al.  Estimating false discovery rates for peptide and protein identification using randomized databases , 2010, Proteomics.

[2]  Dennis B. Troup,et al.  NCBI GEO: archive for functional genomics data sets—10 years on , 2010, Nucleic Acids Res..

[3]  María Martín,et al.  Ongoing and future developments at the Universal Protein Resource , 2010, Nucleic Acids Res..

[4]  Eugene Kolker,et al.  Randomized sequence databases for tandem mass spectrometry peptide and protein identification. , 2005, Omics : a journal of integrative biology.

[5]  Antje Chang,et al.  The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources , 2010, Nucleic Acids Res..

[6]  James A Hill,et al.  ProteomeCommons.org collaborative annotation and project management resource integrated with the Tranche repository. , 2010, Journal of proteome research.

[7]  David Botstein,et al.  Yeast as a Model Organism , 1997, Science.

[8]  Winston Haynes,et al.  Meta-analysis for protein identification: a case study on yeast data. , 2010, Omics : a journal of integrative biology.

[9]  Kimberly Van Auken,et al.  WormBase: a comprehensive resource for nematode research , 2009, Nucleic Acids Res..

[10]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[11]  Tsviya Olender,et al.  GeneCards Version 3: the human gene integrator , 2010, Database J. Biol. Databases Curation.

[12]  Kara Dolinski,et al.  Saccharomyces Genome Database provides mutant phenotype data , 2009, Nucleic Acids Res..

[13]  E. Kolker,et al.  Protein identification and expression analysis using mass spectrometry. , 2006, Trends in microbiology.

[14]  Damian Szklarczyk,et al.  eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations , 2009, Nucleic Acids Res..

[15]  W. Kibbe,et al.  Annotating the human genome with Disease Ontology , 2009, BMC Genomics.

[16]  Florian Gnad,et al.  MAPU 2.0: high-accuracy proteomes mapped to genomes , 2009, Nucleic Acids Res..

[17]  Lennart Martens,et al.  A guide to the Proteomics Identifications Database proteomics data repository , 2009, Proteomics.

[18]  R. Beavis,et al.  A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. , 2003, Analytical chemistry.

[19]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[20]  Winston Haynes,et al.  IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics. , 2011, Journal of proteomics.

[21]  R. Aebersold,et al.  A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.

[22]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[23]  Henry H. N. Lam,et al.  PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows , 2008, EMBO reports.

[24]  Tatiana A. Tatusova,et al.  NCBI Reference Sequences: current status, policy and new initiatives , 2008, Nucleic Acids Res..

[25]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[26]  Robertson Craig,et al.  Open source system for analyzing, validating, and storing protein identification data. , 2004, Journal of proteome research.

[27]  Anushya Muruganujan,et al.  PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium , 2009, Nucleic Acids Res..

[28]  Feng Gao,et al.  Identification of CHI3L1 and MASP2 as a biomarker pair for liver cancer through integrative secretome and transcriptome analysis , 2009, Omics : a journal of integrative biology.

[29]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[30]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

[31]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[32]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[33]  M. Ashburner,et al.  An ontology for cell types , 2005, Genome Biology.

[34]  Naryttza N. Diaz,et al.  The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes , 2005, Nucleic acids research.

[35]  Winston Haynes,et al.  SPIRE: Systematic protein investigative research environment. , 2011, Journal of proteomics.

[36]  Jens M. Rick,et al.  Quantitative mass spectrometry in proteomics: a critical review , 2007, Analytical and bioanalytical chemistry.

[37]  S Hanash,et al.  Mining the plasma proteome for disease applications across seven logs of protein abundance. , 2011, Journal of proteome research.

[38]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[39]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[40]  Ruedi Aebersold,et al.  Advances in Proteome Analysis by Mass Spectrometry* , 2001, The Journal of Biological Chemistry.

[41]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.