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locscale

https://github.com/cryotud/locscale

Science Score: 67.0%

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Created 11 months ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

stability-release-candidate Python 3.11 PyPI PyPI - Downloads License DOI Citation Badge

LocScale-2.0

LocScale-2.0 is an automated map optimisation program performing physics-informed local sharpening and/or density modification of cryo-EM maps with the aim to improve their interpretability. It utilises general properties inherent to electron scattering from biological macromolecules to restrain the sharpening and/or optimisation filter. These can be inferred directly from the experimental density map, or - in legacy mode – provided from an existing atomic model.

What's new in LocScale-2.0?

  • Completely automated process for map optimisation
  • Feature-enhanced maps: Confidence-weighted map optimisation by variational inference.
  • Hybrid sharpening: Reference-based local sharpening with partial (incomplete) models.
  • Model-free sharpening: Reference-based local sharpening without atomic models.
  • LocScale-SURFER: ChimeraX plugin to toggle contextual structure in LocScale maps.
  • Full support for point group symmetry (helical symmetry to follow).

Documentation

[!IMPORTANT] Please visit https://cryotud.github.io/locscale/ for comprehensive documentation, tutorials and troubleshooting.

Installation

We recommend to use Conda for a local working environment. See here for more information on what Conda flavour may be the right choice for you, and here for Conda installation instructions.

[!NOTE] LocScale should run on any CPU system with Linux, OS X or Windows subsytem for Linux (WSL). To run LocScale efficiently in EMmerNet mode requires the availability of a GPU; it is possible to run it on CPUs but computation will be slow(er).

Quick installation We recommend to use [Conda](https://docs.conda.io/en/latest/) for a local working environment. See [here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html#anaconda-or-miniconda) for more information on what Conda flavour may be the right choice for you, and [here](https://www.anaconda.com/products/distribution) for Conda installation instructions. ##### 1. Install `LocScale-2.0` using environment files Download [environment.yml](https://github.com/cryoTUD/locscale/blob/master/environment.yml) to your local computer, navigate to the location you wish to install `Locscale-2.0` at and run the following: ```bash conda env create -f /path/to/environment.yml conda activate locscale2 ``` ##### 2. Install REFMAC5 via CCP4/CCPEM `LocScale` needs a working instance of [REFMAC5](https://www2.mrc-lmb.cam.ac.uk/groups/murshudov/index.html). If you already have CCP4/CCPEM installed check if the path to run `refmac5` is present in your environment. ```bash which refmac5 ``` If no valid path is returned, please install [CCP4](https://www.ccp4.ac.uk/download/) to ensure refmac5 is accessible to the program.
Step-by-step instructions ##### 1. Create and activate a new conda environment ```bash conda create -n locscale python=3.11 conda activate locscale ``` ##### 2. Install parallelisation support and Fortran compiler `LocScale` uses Fortran code to perform symmetry operations and requires a Fortran compiler to be present in your system. You can install `gfortran`, `mpi4py` and `openmpi` from conda-forge. ```bash conda install -c conda-forge gfortran mpi4py openmpi ``` ##### 3. Install REFMAC5 via CCP4/CCPEM The model-based and hybrid map sharpening modes of LocScale need a working instance of [REFMAC5](https://www2.mrc-lmb.cam.ac.uk/groups/murshudov/index.html). If you already have CCP4/CCPEM installed check if the path to run `refmac5` is present in your environment. For model-free sharpening and confidence-aware density modification REFMAC5 is not required. ```bash which refmac5 ``` If no valid path is returned, please install [CCP4](https://www.ccp4.ac.uk/download/) to ensure REFMAC5 is accessible to the program. ##### 4. Install LocScale and dependencies using pip: We recommend using pip for installation. Use pip version 21.3 or later to ensure all packages and their version requirements are met. ```bash pip install locscale ``` >[!NOTE] > ##### Install development version: >If you would like to install the latest development version of locscale, use the following command to install from the git repository. >```bash >pip install git+https://github.com/cryoTUD/locscale.git >``` To install the git repository in editable mode, clone the repository, navigate to the `locscale` directory, and run `pip install -e .` ##### 5. Testing To test functionality after installation, you can run LocScale unit tests using the following command: ```bash locscale test ```

ColabScale

[!TIP] For quick testing or if you have limited compute resources, many functionalities of LocScale-2.0 are available on ColabScale.

Open In Colab

Credits

LoScale 2.0 is facilitated by a number of open-source projects.

  • EMmer: Python library for electron microscopy map and model manipulations. [3-Clause BSD license]
  • FDRthresholding: Tool for FDR-based density thresholding. [3-Clause BSD license]
  • EMDA: Electron Microscopy Data Analytical Toolkit. [MPL2.0 license]
  • Servalcat: Structure refinement and validation for crystallography and SPA. [MPL2.0 license]
  • mrcfile: MRC file I/O. [3-Clause BSD license]

LocScale also makes use of REFMAC5. REFMAC is distributed as part of CCP-EM.

References

If you found LocScale useful for your research, please consider citing it:

- A. Bharadwaj and A.J. Jakobi, Electron scattering properties and their use in cryo-EM map sharpening, Faraday Discussions 240, 168-183 (2022)

Bugs and questions

For bug reports please use the GitHub issue tracker.

Owner

  • Name: AJ-Lab
  • Login: cryoTUD
  • Kind: user
  • Location: Delft en Provence
  • Company: Delft University of Technology

Citation (CITATION.cff) 

abstract: No description provided.
authors:
- affiliation: Delft University of Technology
  family-names: Bharadwaj
  given-names: Alok
  orcid: 0000-0002-7223-5648
- affiliation: Delft University of Technology
  family-names: Jakobi
  given-names: Arjen J.
  orcid: 0000-0002-7761-2027
cff-version: 1.2.0
date-released: '2025-05-22'
doi: 10.5281/zenodo.15488220
identifiers:
- type: swh
  value: swh:1:dir:5e4a911fcc9a7e8321fcba9859a56abd341ca7eb;origin=https://doi.org/10.5281/zenodo.15488219;visit=swh:1:snp:c34571333a9ac951f555138ed0f4f7ed85ddb94b;anchor=swh:1:rel:4796f0695338c03bff81df56e6158aaa62a31c6d;path=cryoTUD-locscale-364e4b4
license:
- cc-by-4.0
message: If you use this software, please cite it as below.
repository-code: https://github.com/cryoTUD/locscale/tree/v2.3.1
title: "LocScale 2.0 \u2013 confidence-weighted cryoEM map optimisation"
type: software
version: v2.3.1

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Dependencies

docs/requirements.txt pypi
  • mkdocs-material *
pyproject.toml pypi
setup.py pypi
environment.yml conda
  • gfortran
  • mpi4py
  • numpy
  • openmpi
  • pip
  • python 3.11.*