Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction

BMC Bioinformatics

Table 2 The average similarity metric scores (SSIM) for the fully automated single-particle alignment

SSIM approach	Similarity	Time consuming
Default registration	99.648	2.19
Adjusted initial radius	99.754	2.06
Adjusted initial radius, maximum iterations	99.819	5.51
Similarity transformation model	99.561	5.16
Affine model based on similarity initial condition	99.627	5.10

Registration method 1 is based on the “default registration model” which registers the two particle images using affine transformation to solve the distortion between the two images includes scaling, rotation. Registration method 2 is based on the “adjusted initial radius model” which improves the particle image registration by adjusting the optimizer and metric configuration properties. Registration method3 is based on the “adjusted initial radius-based maximum iterations model” in which the optimizer controls the maximum number of iterations that the optimizer will be allowed to take. Also allows the registration search to run longer and potentially find better registration results. Registration method 4 is based on the “affine model-based on similarity initial condition model” which registers the particle images by using an “affine” transformation model with the “similarity” results used as an initial condition for the geometric transformation. This model is refined estimate for the registration includes the possibility of shear

ISSN: 1471-2105