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  Learning torus PCA-based classification for multiscale RNA correction with application to SARS-CoV-2

Wiechers, H., Eltzner, B., Mardia, K. V., & Huckemann, S. F. (2023). Learning torus PCA-based classification for multiscale RNA correction with application to SARS-CoV-2. Journal of the Royal Statistical Society - Series C: Applied Statistics, 72(2), 271-293. doi:10.1093/jrsssc/qlad004.

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 Creators:
Wiechers, Henrik, Author
Eltzner, Benjamin1, Author           
Mardia, Kanti V., Author
Huckemann, Stephan F., Author
Affiliations:
1Research Group of Computational Biomolecular Dynamics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society, ou_3350134              

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Free keywords: angular shape analysis, clash correction, frameshift stimulation element, Fréchet and Procrustes means, mesoscopic shape and microscopic shape, size-and-shape space
 Abstract: Three-dimensional RNA structures frequently contain atomic clashes. Usually, corrections approximate the biophysical chemistry, which is computationally intensive and often does not correct all clashes. We propose fast, data-driven reconstructions from clash-free benchmark data with two-scale shape analysis: microscopic (suites) dihedral backbone angles, mesoscopic sugar ring centre landmarks. Our analysis relates concentrated mesoscopic scale neighbourhoods to microscopic scale clusters, correcting within-suite-backbone-to-backbone clashes exploiting angular shape and size-and-shape Fréchet means. Validation shows that learned classes highly correspond with literature clusters and reconstructions are well within physical resolution. We illustrate the power of our method using cutting-edge SARS-CoV-2 RNA.

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Language(s): eng - English
 Dates: 2023-03-24
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1093/jrsssc/qlad004
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Title: Journal of the Royal Statistical Society - Series C: Applied Statistics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 72 (2) Sequence Number: - Start / End Page: 271 - 293 Identifier: Other: ISSN
CoNE: https://pure.mpg.de/cone/journals/resource/1467-9876