Journal of Structural Biology
Journal of Structural Biology
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发表时间：
2004
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通讯作者：
Jacob Bruggemann;Gabriel C. Lander;Andrew I. Su
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作者：
Jacob Bruggemann;Gabriel C. Lander;Andrew I. Su
从冷冻电子显微镜 (cryo-EM) 显微照片中提取粒子是处理单粒子数据集的关键步骤。尽管已经开发了自动粒子拾取算法，但这些算法通常依赖于粒子识别的二维模板，这可能会表现出偏差，从而通过重建管道传播伪影。手动拾取被视为颗粒选择的黄金标准解决方案，但在数千张图像的数据集上执行它太耗时。近年来，事实证明，众包在利用开放网络手动管理数据集方面非常有效。特别是，诸如银河动物园之类的公民科学项目已经展示了吸引用户处理大量数据的科学兴趣的力量。为此，我们探索了众包在冷冻电镜粒子挑选中的可能应用，提出了各种新颖的实验，包括由未经培训的公民科学家制作完全注释的粒子集。我们展示了众包颗粒选择任务的可能性和局限性，并探索了众包冷冻电镜数据处理的更多选项。
Extraction of particles from cryo-electron microscopy (cryo-EM) micrographs is a crucial step in processing single-particle datasets. Although algorithms have been developed for automatic particle picking, these algorithms generally rely on two-dimensional templates for particle identi fi cation, which may exhibit biases that can propagate artifacts through the reconstruction pipeline. Manual picking is viewed as a gold-standard solution for particle selection, but it is too time-consuming to perform on data sets of thousands of images. In recent years, crowdsourcing has proven e ff ective at leveraging the open web to manually curate datasets. In particular, citizen science projects such as Galaxy Zoo have shown the power of appealing to users ’ scienti fi c interests to process enormous amounts of data. To this end, we explored the possible applications of crowdsourcing in cryo-EM particle picking, presenting a variety of novel experiments including the production of a fully annotated particle set from untrained citizen scientists. We show the possibilities and limitations of crowdsourcing particle selection tasks, and explore further options for crowdsourcing cryo-EM data processing.