A stochastic hill climbing approach for simultaneous 2D alignment and clustering of cryogenic electron microscopy images

Research output: Contribution to journalArticleResearchpeer-review

Abstract

A critical step in the analysis of novel cryogenic electron microscopy (cryo-EM) single-particle datasets is the identification of homogeneous subsets of images. Methods for solving this problem are important for data quality assessment, ab initio 3D reconstruction, and analysis of population diversity due to the heterogeneous nature of macromolecules. Here we formulate a stochastic algorithm for identification of homogeneous subsets of images. The purpose of the method is to generate improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion. We show that our method overcomes inherent limitations of widely used clustering approaches and proceed to test the approach on six publicly available experimental cryo-EM datasets. We conclude that, in each instance, ab initio 3D reconstructions of quality suitable for initialization of high-resolution refinement are produced from the cluster centers.
Original languageEnglish
Pages (from-to)988-996
Number of pages9
JournalStructure
Volume24
Issue number6
DOIs
Publication statusPublished - 7 Jun 2016

Keywords

  • cryo-EM
  • single-particle
  • electron microscopy
  • clustering
  • alignment
  • stochastic

Cite this

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title = "A stochastic hill climbing approach for simultaneous 2D alignment and clustering of cryogenic electron microscopy images",
abstract = "A critical step in the analysis of novel cryogenic electron microscopy (cryo-EM) single-particle datasets is the identification of homogeneous subsets of images. Methods for solving this problem are important for data quality assessment, ab initio 3D reconstruction, and analysis of population diversity due to the heterogeneous nature of macromolecules. Here we formulate a stochastic algorithm for identification of homogeneous subsets of images. The purpose of the method is to generate improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion. We show that our method overcomes inherent limitations of widely used clustering approaches and proceed to test the approach on six publicly available experimental cryo-EM datasets. We conclude that, in each instance, ab initio 3D reconstructions of quality suitable for initialization of high-resolution refinement are produced from the cluster centers.",
keywords = "cryo-EM, single-particle, electron microscopy, clustering, alignment, stochastic",
author = "Reboul, {Cyril F.} and Frederic Bonnet and Dominika Elmlund and Hans Elmlund",
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journal = "Structure",
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A stochastic hill climbing approach for simultaneous 2D alignment and clustering of cryogenic electron microscopy images. / Reboul, Cyril F.; Bonnet, Frederic; Elmlund, Dominika; Elmlund, Hans.

In: Structure, Vol. 24, No. 6, 07.06.2016, p. 988-996.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A stochastic hill climbing approach for simultaneous 2D alignment and clustering of cryogenic electron microscopy images

AU - Reboul, Cyril F.

AU - Bonnet, Frederic

AU - Elmlund, Dominika

AU - Elmlund, Hans

PY - 2016/6/7

Y1 - 2016/6/7

N2 - A critical step in the analysis of novel cryogenic electron microscopy (cryo-EM) single-particle datasets is the identification of homogeneous subsets of images. Methods for solving this problem are important for data quality assessment, ab initio 3D reconstruction, and analysis of population diversity due to the heterogeneous nature of macromolecules. Here we formulate a stochastic algorithm for identification of homogeneous subsets of images. The purpose of the method is to generate improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion. We show that our method overcomes inherent limitations of widely used clustering approaches and proceed to test the approach on six publicly available experimental cryo-EM datasets. We conclude that, in each instance, ab initio 3D reconstructions of quality suitable for initialization of high-resolution refinement are produced from the cluster centers.

AB - A critical step in the analysis of novel cryogenic electron microscopy (cryo-EM) single-particle datasets is the identification of homogeneous subsets of images. Methods for solving this problem are important for data quality assessment, ab initio 3D reconstruction, and analysis of population diversity due to the heterogeneous nature of macromolecules. Here we formulate a stochastic algorithm for identification of homogeneous subsets of images. The purpose of the method is to generate improved 2D class averages that can be used to produce a reliable 3D starting model in a rapid and unbiased fashion. We show that our method overcomes inherent limitations of widely used clustering approaches and proceed to test the approach on six publicly available experimental cryo-EM datasets. We conclude that, in each instance, ab initio 3D reconstructions of quality suitable for initialization of high-resolution refinement are produced from the cluster centers.

KW - cryo-EM

KW - single-particle

KW - electron microscopy

KW - clustering

KW - alignment

KW - stochastic

UR - http://www.ncbi.nlm.nih.gov/pubmed/27184214

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