Subtomogram alignment by adaptive Fourier coefficient thresholding
Subtomogram alignment by adaptive Fourier coefficient thresholding
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DOI：
10.1016/j.jsb.2010.05.013
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发表时间：
2010-09-01
影响因子：
3
通讯作者：
Horowitz, Mark
中科院分区：
文献类型：
--
作者：
Amat, Fernando;Comolli, Luis R.;Horowitz, Mark
在过去的几年中，三维（3D）子断层图像对齐已成为一个重要的工具，在冷冻电子断层扫描（CET）。这种技术允许产生更高分辨率的图像的结构，不能使用单粒子方法重建。在以前工作的基础上，我们提出了一种新的子断层图像之间的相异性测量，适用于CET图像中经常出现的噪声图像。对噪声更鲁棒的技术提供了分析较厚样品（例如全细胞）中的大分子或降低较薄样品中的散焦以推动对比度传递函数（CTF）的第一个零点的能力。本文提出的阈值约束互相关（Threshold Constrained Cross-Correlation，TCCC）方法利用噪声的统计特性，自动选择傅立叶系数中的一小部分来计算互相关，它有两个主要优点：第一，通过只考虑信号占主导地位的峰，减少了噪声的影响;第二，它避免了丢失的楔形归一化问题，因为我们考虑了所有可能的子断层图像对的相同数量的系数。我们目前的结果与合成和真实的数据比较，我们的方法与其他现有的方法在不同的SNR和丢失的楔形条件下，并表明，TCCC提高对齐结果的SNR < 0.1的数据集。我们已经将源代码免费提供给社区。(C)2010年爱思唯尔公司All rights reserved.
In the past few years, three-dimensional (3D) subtomogram alignment has become an important tool in cryo-electron tomography (CET). This technique allows one to produce higher resolution images of structures which can not be reconstructed using single-particle methods. Building on previous work, we present a new dissimilarity measure between subtomograms that works well for the noisy images that often occur in CET images. A technique that is more robust to noise provides the ability to analyze macromolecules in thicker samples such as whole cells or lower the defocus in thinner samples to push the first zero of the Contrast Transfer Function (CTF). Our method, Threshold Constrained Cross-Correlation (TCCC), uses statistics of the noise to automatically select only a small percentage of the Fourier coefficients to compute the cross-correlation, which has two main advantages: first, it reduces the influence of the noise by looking at only those peaks dominated by signal; and second, it avoids the missing wedge normalization problem since we consider the same number of coefficients for all possible pairs of subtomograms. We present results with synthetic and real data to compare our approach with other existing methods under different SNR and missing wedge conditions, and show that TCCC improves alignment results for datasets with SNR < 0.1. We have made our source code freely available for the community. (C) 2010 Elsevier Inc. All rights reserved.