Chan et al. BMC Genomics 2011, 12:290 http://www.biomedcentral.com/1471-2164/12/290 S O F T W A R E Open Access A honey bee (Apis mellifera L.) PeptideAtlas crossing castes and tissues Queenie WT Chan1, Robert Parker1, Zhi Sun2, Eric W Deutsch2† and Leonard J Foster1*† Abstract Background: Honey bees are a mainstay of agriculture, contributing billions of dollars through their pollination activities. Bees have been a model system for sociality and group behavior for decades but only recently have molecular techniques been brought to study this fascinating and valuable organism. With the release of the first draft of its genome in 2006, proteomics of bees became feasible and over the past five years we have amassed in excess of 5E+6 MS/MS spectra. The lack of a consolidated platform to organize this massive resource hampers our ability, and that of others, to mine the information to its maximum potential. Results: Here we introduce the Honey Bee PeptideAtlas, a web-based resource for visualizing mass spectrometry data across experiments, providing protein descriptions and Gene Ontology annotations where possible. We anticipate that this will be helpful in planning proteomics experiments, especially in the selection of transitions for selected reaction monitoring. Through a proteogenomics effort, we have used MS/MS data to anchor the annotation of previously undescribed genes and to re-annotate previous gene models in order to improve the current genome annotation. Conclusions: The Honey Bee PeptideAtlas will contribute to the efficiency of bee proteomics and accelerate our understanding of this species. This publicly accessible and interactive database is an important framework for the current and future analysis of mass spectrometry data. Background The honey bee, Apis mellifera L., best known for its honey production and pollination of crops, has been making headlines in the past half-decade. Under increas- ing threat from disease and chemical residues in the environment, research efforts on this beneficial insect have escalated in the past five years. After completion of the honey bee genome sequence in 2006 [1], the next step is to understand the proteome. Implicit in this goal is that the expressed proteome of the bee must first be defined: bioinformatic analyses place the number of honey bee genes near 10,000, (summarized in [2]) but only a handful of these have been observed experimen- tally at the protein level. Since most gene prediction algorithms require a training set of genes with well- established translation start/stop sites and intron-exon * Correspondence: foster@chibi.ubc.ca † Contributed equally 1Centre for High-Throughput Biology and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, Canada Full list of author information is available at the end of the article boundaries, the lack of even a hundred bee genes with real experimental evidence, let alone annotated to this level of quality, hinders prediction efforts. Homology- based methods using Drosophila and other organisms with well-annotated gene lists have helped to find core bee genes, yet the relatively large evolutionary distance between Drosophila and Apis (~300 million years) poses limitations on this approach. For example, the eusocial- ity of bees implies the expression of genes that flies, being solitary insects, would not have. mRNA sequences, such as from EST libraries, can help to map transcribed genes but experimentally verified proteins are still the ultimate affirmation of gene expression. The emerging field of proteogenomics [3,4] applies the power of mass spectrometry proteomics to improve the genomic understanding for a species. Typically large proteomics datasets are processed using either a large set of ORF overpredictions or using the entire genome itself in order to identify sequences that are translated to protein but do not yet appear in annotated protein lists. This technique is computationally expensive, but a © 2011 Chan et al; licensee BioMed Central Ltd. 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.