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首页> 外文期刊>Journal of Structural Biology >APPLE picker: Automatic particle picking, a low-effort cryo-EM framework
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APPLE picker: Automatic particle picking, a low-effort cryo-EM framework

机译:Apple选择器:自动粒子拣选,低努力Cryo-EM框架

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摘要

Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of beta-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.
机译:颗粒拣选是单颗粒冷冻电子显微镜(Cryo-EM)计算管道的关键第一步。 从显微照片中选择颗粒尤其是具有低对比度的小颗粒。 随着高分辨率的重建通常需要数十万种颗粒,手动挑选许多颗粒通常太耗时。 虽然基于模板的粒子拣选是目前一种流行的方法,但它可能会影响手动偏压进入选择过程。 此外,这种方法仍然有点耗时。 本文介绍了苹果(具有低用户努力的自动粒子采摘)拾取器,简单而新颖的快速,准确和无模板颗粒拣选。 在含有β-半乳糖苷酶的显微照片,T20S蛋白酶体,70s核糖体和髓候颗粒血晶素突起的公开可用数据集上评估该方法。

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