Fast maximum-likelihood refinement of electron microscopy images

Bioinformatics. 2005 Sep 1:21 Suppl 2:ii243-4. doi: 10.1093/bioinformatics/bti1140.

Abstract

Motivation: Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization.

Results: We speeded up ML image refinement by reducing its search space over the alignment parameters. Application of this reduced-search approach to a cryo-EM dataset yielded practically identical results as the original approach, but in approximately one day instead of one week of CPU.

Availability: This work has been implemented in the public domain package Xmipp. Documentation and download instructions may be found at: http://www.cnb.uam.es/~bioinfo

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Cryoelectron Microscopy / methods*
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Likelihood Functions
  • Reproducibility of Results
  • Sensitivity and Specificity