DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
DeepRes: a new deep-learning- and aspect-based local resolution method for electron-microscopy maps
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DOI：
10.1107/s2052252519011692
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发表时间：
2019-11-01
期刊：
影响因子：
3.9
通讯作者：
Sorzano, Carlos Oscar S.
中科院分区：
文献类型：
--
作者：
Ramirez-Aportela, Erney;Mota, Javier;Sorzano, Carlos Oscar S.
在这篇文章中，提出了一种方法来估计一个新的局部质量措施的三维cryoEM地图，采用的形式的“局部分辨率”类型的信息。该算法（DeepRes）基于深度学习3D特征检测。DeepRes是全自动和无参数的，避免了大多数当前方法的问题，例如它们对B因子锐化（除非改变3D掩模）的增强不敏感，等等，这是一个迄今为止在cryoEM领域几乎被忽视的问题。通过这种方式，DeepRes可以应用于任何地图，在应用增强过程（如各向同性滤波器）或更复杂的过程（如基于模型的局部锐化，非基于模型的方法或去噪）后检测局部质量的细微变化，这些过程可能很难使用当前方法。它的表现就像人类观察者所期望的那样。与传统的本地分辨率指标的比较也得到了解决。
In this article, a method is presented to estimate a new local quality measure for 3D cryoEM maps that adopts the form of a 'local resolution' type of information. The algorithm (DeepRes) is based on deep-learning 3D feature detection. DeepRes is fully automatic and parameter-free, and avoids the issues of most current methods, such as their insensitivity to enhancements owing to B-factor sharpening (unless the 3D mask is changed), among others, which is an issue that has been virtually neglected in the cryoEM field until now. In this way, DeepRes can be applied to any map, detecting subtle changes in local quality after applying enhancement processes such as isotropic filters or substantially more complex procedures, such as model-based local sharpening, non-model-based methods or denoising, that may be very difficult to follow using current methods. It performs as a human observer expects. The comparison with traditional local resolution indicators is also addressed.