This repository contains the Python software implementation of ManifoldEM for determination of conformational continua of macromolecules from single-particle cryo-EM data, as was first introduced by Dashti, et al. (2014). A detailed user manual is provided on the main branch of this repository. Carefully going through this manual will prepare you for running ManifoldEM on your own data sets. If you have any questions about ManifoldEM after reading this entire document, carefully check the ManifoldEM GitHub forum for similar inquiries or, if no similar posts exist, create a new thread detailing your inquiry.

This software was developed in the Frank research group at Columbia University (https://joachimfranklab.org) in collaboration with members from UWM (see below). The following resources may prove useful for a review of ManifoldEM history, theory and implementations:

1. Dashti, A. et al. Trajectories of the ribosome as a Brownian nanomachine. PNAS, 2014.
2. Dashti, A. et al. Retrieving functional pathways of biomolecules from single-particle snapshots. Nature Communications, 2020.
3. Mashayekhi, G. ManifoldEM Matlab repository. https://github.com/GMashayekhi/ManifoldEM_Matlab
4. Seitz, E. et al. Geometric machine learning informed by ground truth: Recovery of conformational continuum from single-particle cryo-EM data of biomolecules. bioRxiv, 2021.

ManifoldEM Python team (alphabetically ordered):

• Ali Dashti, University of Wisconsin-Milwaukee
• Joachim Frank, Columbia University
• Hstau Liao, Columbia University
• Suvrajit Maji, Columbia University
• Ghoncheh Mashayekhi, University of Wisconsin-Milwaukee
• Abbas Ourmazd, University of Wisconsin-Milwaukee
• Peter Schwander, University of Wisconsin-Milwaukee
• Evan Seitz, Columbia University

Individual author contributions to code are provided in the headers of each script.

If you find this code useful in your work, please cite

{E. Seitz et al., "ManifoldEM Python repository," Zenodo, 2021, doi: 10.5281/zenodo.5578874}

Copyright 2021

The software, code sample and their documentation made available on this repository could include technical or other mistakes, inaccuracies or typographical errors. We may make changes to this software or documentation at any time without prior notice, and we assume no responsibility for errors or omissions therein.

For further details, please see the LICENSE file.