Exploring applications of crowdsourcing to cryo-EM
Exploring applications of crowdsourcing to cryo-EM
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DOI：
10.1016/j.jsb.2018.02.006
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发表时间：
2018-07-01
影响因子：
3
通讯作者：
Su, Andrew, I
中科院分区：
文献类型：
--
作者：
Bruggemann, Jacob;Lander, Gabriel C.;Su, Andrew, I
从低温电子显微镜（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。虽然已经开发了用于自动颗粒拾取的算法，但这些算法通常依赖于二维模板进行颗粒识别，这可能会出现偏差，从而通过重建管道传播伪影。人工选择被认为是粒子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过于耗时。近年来，众包在利用开放网络手动管理数据集方面已被证明是有效的。特别是，像银河动物园这样的公民科学项目已经显示出吸引用户的科学兴趣来处理大量数据的力量。为此，我们探索了众包在低温电镜粒子采集中的可能应用，提出了各种新颖的实验，包括由未经训练的公民科学家生产完全注释的粒子集。我们展示了众包粒子选择任务的可能性和局限性，并探索了众包低温电镜数据处理的进一步选择。
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 identification, 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 effective 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' scientific 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.