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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 16, 2026

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions
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Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Published on: January 7, 2019

Multi-reference-based multiple alignment statistics enables accurate protein-particle pickup from noisy images.

Masaaki Kawata1, Chikara Sato

  • 1National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Tsukuba 305-8568, Japan.

Microscopy (Oxford, England)
|November 23, 2012
PubMed
Summary

We developed new methods, MRA-StoPICK and MRMA-StoPICK, for accurate particle picking from noisy images. MRMA-StoPICK excels in extremely noisy data, enabling high-resolution 3D structure analysis.

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Area of Science:

  • Scientific Imaging
  • Computational Biology
  • Data Science

Background:

  • Automated particle picking from noisy electron micrographs is crucial for high-resolution single particle analysis.
  • Traditional reference-based matching can fail in very noisy images, leading to inaccurate particle identification.
  • Statistical image processing offers a promising approach for analyzing noisy scientific data.

Purpose of the Study:

  • To develop advanced statistical methods for accurate particle pickup from extremely noisy images.
  • To improve the reliability of particle identification in single particle analysis.
  • To enable high-resolution 3D structure determination of particles from low signal-to-noise ratio data.

Main Methods:

  • Developed density-based peak search and average peak height selection using multi-reference alignment (MRA).
  • Extended the method to multi-reference multiple alignment (MRMA) for enhanced accuracy.
  • Introduced stochastic pickup with MRA (MRA-StoPICK) and MRMA (MRMA-StoPICK).

Main Results:

  • MRMA-StoPICK achieved higher pickup accuracy, even from images with a signal-to-noise ratio of 0.001.
  • The MRMA-StoPICK method demonstrated robustness and independence from parameter settings.
  • Successfully applied MRA-StoPICK and MRMA-StoPICK to cryo-electron micrographs of Rice dwarf virus.

Conclusions:

  • MRA-StoPICK and MRMA-StoPICK provide accurate particle pickup from noisy electron micrographs.
  • These computational methods are feasible with current resources for timely analysis.
  • The developed techniques are expected to advance high-resolution 3D structure analysis in various scientific fields.