Aspire v0.13.1

Algorithms for Single Particle Reconstruction

Our project aims to develop mathematical theory, improved algorithms, and software for 3-D molecular structure determination using cryo-EM. Our methods use a combination of tools from different areas of mathematics, statistics, and computer science, such as computerized tomography, optimization (convex and non-convex), random matrix theory, signal and image processing, linear and nonlinear dimensionality reduction, randomized algorithms in numerical linear algebra, and representation theory.

Contents

• Installation
  • Install Conda
  • Getting Started - Installation
  • Test the package
  • Optimized Numerical Backends
  • Installing GPU Extensions
  • Generating Documentation
• Getting Started - CLI
  • 1. Particle-Picking
  • 2. Particle Extraction
  • 3. Estimate Contrast Transfer Function
  • 4. Image Preprocessing
  • 5. Image Denoising
  • 6. Orientation Estimation
  • 7. Reconstructing a mean volume with covariance
  • Arguments, options and flags
• Class Averaging Architecture
  • ClassAvgSource
  • Classifiers
  • Class Selectors
  • Averagers
• Modules
  • aspire package
• Authors

Galleries:

• Tutorials
  • API Introduction
  • Component Examples
• Experiments

Indices and tables

• Index

• Module Index

• Search Page

References

[HAndenS18]

Ayelet Heimowitz, Joakim Andén, and Amit Singer. APPLE picker: automatic particle picking, a low-effort cryo-em framework. CoRR, 2018. URL: http://arxiv.org/abs/1802.00469, arXiv:1802.00469.

[SS12]

Yoel Shkolnisky and Amit Singer. Viewing direction estimation in cryo-em using synchronization. SIAM J. Imaging Sci., 5(3):1088–1110, 2012. URL: https://doi.org/10.1137/120863642, doi:10.1137/120863642.

[SS11]

Amit Singer and Yoel Shkolnisky. Three-dimensional structure determination from common lines in cryo-em by eigenvectors and semidefinite programming. SIAM J. Imaging Sciences, 4(2):543–572, 2011. URL: https://doi.org/10.1137/090767777, doi:10.1137/090767777.