Paper Publications

Impact Factor:0.0

Journal:PLoS One

Abstract:Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Indexed by:Journal paper

Discipline:Natural Science

Document Type:J

Volume:12

Issue:8

Page Number:e0182130

Translation or Not:no

Date of Publication:2017-08-07

Included Journals:SCI

Links to published journals:http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182130

First Author:Jiayi Wu

Correspondence Author:Youdong Mao

All the Authors:Yong-Bei Ma,Charles Congdon,Bevin Brett,Shuobing Chen,Yaofang Xu,Qi Ouyang