CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting
In order to run CATBOSS, you will need the following dependencies:
- The SIMPLEchangepoint Python package (https://github.com/DEShawResearch/SIMPLEchangepoint) [1]
- The MATLAB wrapper for Rubner's earth mover's distance calculation (https://github.com/garydoranjr/pyemd), or FastEMD [2,3]
Data generated by other research groups needed to reproduce results from the paper are available freely or upon request from these groups:
- BPTI: https://www.deshawresearch.com/downloads/download_trajectory_science2010.cgi/ (trajectory), https://pubs.acs.org/doi/10.1021/acs.jctc.0c00604 (benchmark clustering) [4,5]
- HP35: https://www.deshawresearch.com/downloads/download_trajectory_pnas2012.cgi/ (trajectory), https://github.com/moldyn (dihedral trajectory and clustering) [6,7]
- Prepare the dataset to be analyzed in space-delimited format, with the first column being time, and the remaining columns being the variables of the dataset. You may also use sample data provided here for your convenience.
- Run CATBOSS_detectChanges.py <input_file> --lam --alpha (Optional: Import the change probability matrix and data files into your MATLAB workspace and run CATBOSS_slopeAnalysis.m to identify sloped segments)
- Import the change probability matrix and data files into your MATLAB workspace and run CATBOSS_distanceMatrix.m
- Import the distance matrix into your MATLAB workspace and run CATBOSS_cluster.m
- Import the resulting cluster assignment into your MATLAB workspace and run CATBOSS_mapToPoints.m to get final assignments for each data point.
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@article {Fan7454, author = {Fan, Zhou and Dror, Ron O. and Mildorf, Thomas J. and Piana, Stefano and Shaw, David E.}, title = {Identifying localized changes in large systems: Change-point detection for biomolecular simulations}, volume = {112}, number = {24}, pages = {7454--7459}, year = {2015}, doi = {10.1073/pnas.1415846112}, publisher = {National Academy of Sciences}, issn = {0027-8424}, URL = {https://www.pnas.org/content/112/24/7454}, eprint = {https://www.pnas.org/content/112/24/7454.full.pdf}, journal = {Proceedings of the National Academy of Sciences} }
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@Misc{, author = {Gary Doran}, title = {{PyEMD}: Earth Mover's Distance for {Python}}, year = {2014--}, url = "https://github.com/garydoranjr/pyemd", note = {[Online; accessed ]} }
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@INPROCEEDINGS{Pele-eccv2008, author = {Ofir Pele and Michael Werman}, title = {A Linear Time Histogram Metric for Improved SIFT Matching}, booktitle = {ECCV}, year = {2008} }
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@article{doi:10.1126/science.1187409, author = {David E. Shaw and Paul Maragakis and Kresten Lindorff-Larsen and Stefano Piana and Ron O. Dror and Michael P. Eastwood and Joseph A. Bank and John M. Jumper and John K. Salmon and Yibing Shan and Willy Wriggers }, title = {Atomic-Level Characterization of the Structural Dynamics of Proteins}, journal = {Science}, volume = {330}, number = {6002}, pages = {341-346}, year = {2010}, doi = {10.1126/science.1187409} }
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@article{doi:10.1021/acs.jctc.0c00604, author = {Cocina, Francesco and Vitalis, Andreas and Caflisch, Amedeo}, title = {Sapphire-Based Clustering}, journal = {Journal of Chemical Theory and Computation}, volume = {16}, number = {10}, pages = {6383-6396}, year = {2020}, doi = {10.1021/acs.jctc.0c00604} }
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@article {ChiavazzoE5494, author = {Chiavazzo, Eliodoro and Covino, Roberto and Coifman, Ronald R. and Gear, C. William and Georgiou, Anastasia S. and Hummer, Gerhard and Kevrekidis, Ioannis G.}, title = {Intrinsic map dynamics exploration for uncharted effective free-energy landscapes}, volume = {114}, number = {28}, pages = {E5494--E5503}, year = {2017}, doi = {10.1073/pnas.1621481114}, publisher = {National Academy of Sciences} }
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@article{doi:10.1021/jp410398a, author = {Jain, Abhinav and Stock, Gerhard}, title = {Hierarchical Folding Free Energy Landscape of HP35 Revealed by Most Probable Path Clustering}, journal = {The Journal of Physical Chemistry B}, volume = {118}, number = {28}, pages = {7750-7760}, year = {2014}, doi = {10.1021/jp410398a} }
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