English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cryo-electron microscopy (cryo-EM) studies using single particlereconstruction are extensively used to reveal structural information onmacromolecular complexes. Aiming at the highest achievable resolution, state ofthe art electron microscopes automatically acquire thousands of high-qualitymicrographs. Particles are detected on and boxed out from each micrograph usingfully- or semi-automated approaches. However, the obtained particles stillrequire laborious manual post-picking classification, which is one majorbottleneck for single particle analysis of large datasets. We introduce MAPPOS,a supervised post-picking strategy for the classification of boxed particleimages, as additional strategy adding to the already efficient automatedparticle picking routines. MAPPOS employs machine learning techniques to traina robust classifier from a small number of characteristic image features. Inorder to accurately quantify the performance of MAPPOS we used simulatedparticle and non-particle images. In addition, we verified our method byapplying it to an experimental cryo-EM dataset and comparing the results to themanual classification of the same dataset. Comparisons between MAPPOS andmanual post-picking classification by several human experts demonstrated thatmerely a few hundred sample images are sufficient for MAPPOS to classify anentire dataset with a human-like performance. MAPPOS was shown to greatlyaccelerate the throughput of large datasets by reducing the manual workload byorders of magnitude while maintaining a reliable identification of non-particleimages.

Automatic post-picking using MAPPOS improves particle image detection from Cryo-EM micrographs