Centre for High-Throughput Biology and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, Canada.
BMC Genomics. 2011 Jun 3;12:290. doi: 10.1186/1471-2164-12-290.
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.
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.
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.
蜜蜂是农业的主要支柱,通过其授粉活动贡献了数十亿美元。几十年来,蜜蜂一直是社会性和群体行为的模式系统,但直到最近,分子技术才被用于研究这种迷人而有价值的生物。随着 2006 年其基因组初稿的发布,蜜蜂的蛋白质组学变得可行,在过去的五年中,我们积累了超过 5E+6 的 MS/MS 光谱。缺乏整合平台来组织这个庞大的资源,阻碍了我们(和其他人)挖掘信息以发挥其最大潜力的能力。
在这里,我们介绍了蜜蜂肽图谱,这是一个基于网络的资源,用于跨实验可视化质谱数据,提供蛋白质描述和可能的基因本体论注释。我们预计这将有助于规划蛋白质组学实验,特别是在选择用于选择反应监测的转换时。通过一个蛋白质基因组学的努力,我们使用 MS/MS 数据来确定以前未描述基因的注释,并重新注释以前的基因模型,以提高当前的基因组注释。
蜜蜂肽图谱将有助于提高蜜蜂蛋白质组学的效率,并加速我们对该物种的理解。这个可公开访问和交互式的数据库是当前和未来分析质谱数据的重要框架。