The ROME (Refinement and Optimization via Machine lEarning for cryo-EM) software package is one of the major research products at the Intel® PCCSB. The ROME package is a parallel computing software system dedicated for high-resolution cryo-EM structure determination and data analysis, which implements advanced machine learning approaches in modern computer sciences and runs natively in an HPC environment. The ROME 1.0 introduces SML (statistical manifold learning)-based deep classification following MAP-based image alignment. It also implemented traditional unsupervised MAP-based classification and includes several useful tools, such as 2D class averaging with CTF (contrast transfer function) correction and a convenient GUI for curation, inspection and verification of single-particle classes. The ROME system has be optimized on both Intel® Xeon multi-core CPUs and Intel® Xeon Phi many-core coprocessors. The future upgrade of the ROME system will introduce more advanced machine-learning algorithms for enabling methods in high-performance cryo-EM data analysis, including nonlinear dimensionality reduction, deep learning and 4D reconstructions.
Reference (Please cite the following article should you use ROME in your study)
J. Wu, Y. Ma, C. Congdon, B. Brett, S. Chen, Q. Ouyang, Y. Mao. Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. PLoS ONE 12, e0182130 (2017). https://doi.org/10.1371/ journal.pone.0182130. arXiv: 1604.04539. Download
Please cite the following articles should you use MAP program in ROME
S.H.W. Scheres. A Bayesian view on cryo-EM structure determination. J. Mol. Biol. 415, 406-418 (2012).
F.J. Sigworth. A maximum-likelihood approach to single-particle image refinement. J Struct Biol 122, 328-339 (1998).