MonoRes
MonoRes computes local resolution in three-dimensional electron microscopy (EM) maps using the monogenic signal framework to assess resolution at each voxel.
Key Features:
- Monogenic signal framework: Extends the analytic signal concept by employing the monogenic signal to analyze local amplitudes in 3D EM maps.
- Automatic operation: Operates autonomously without requiring user-defined parameters.
- Frequency filtering: Filters the 3D EM map at different frequencies to obtain scale-dependent information.
- Amplitude-based voxel-wise criterion: Computes the amplitude of the monogenic signal across frequencies and applies an amplitude-based criterion to determine resolution at each voxel.
- Local filtering option: Optionally applies local filtering of the original map using the computed local resolution values.
- Computational efficiency: Performs resolution estimation more quickly than existing methods.
- High accuracy: Demonstrates high accuracy in determining local resolution across validation tests.
Scientific Applications:
- Model building and validation: Provides voxel-wise resolution estimates to guide model building and validation from 3D EM maps.
- Interpretation of molecular structures: Reveals spatial variability of resolution within maps to aid interpretation of molecular features at different scales.
Methodology:
Filter the 3D EM map at different frequencies, compute the amplitude of the monogenic signal at each frequency, apply an amplitude-based criterion to assign a resolution value to each voxel, and optionally locally filter the original map using the computed local resolution values.
Topics
Details
- License:
- Freeware
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- plugin
- Added:
- 5/18/2018
- Last Updated:
- 11/24/2024
Operations
Publications
Vilas JL, Gómez-Blanco J, Conesa P, Melero R, Miguel de la Rosa-Trevín J, Otón J, Cuenca J, Marabini R, Carazo JM, Vargas J, Sorzano COS. MonoRes: Automatic and Accurate Estimation of Local Resolution for Electron Microscopy Maps. Structure. 2018;26(2):337-344.e4. doi:10.1016/j.str.2017.12.018. PMID:29395788.