Wiechers, H, Eltzner, B, Mardia, KV orcid.org/0000-0003-0090-6235 et al. (1 more author) (2023) Learning torus PCA based classification for multiscale RNA correction with application to SARS-CoV-2. Journal of the Royal Statistical Society: Series C, 72 (2). pp. 271-293. ISSN 0035-9254
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © (RSS) Royal Statistical Society 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https:// creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | angular shape analysis, clash correction, frameshift stimulation element, Fréchet and Procrustes means, mesoscopic shape and microscopic shape, size-and-shape space |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Jan 2023 11:45 |
Last Modified: | 05 Oct 2023 12:49 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/jrsssc/qlad004 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194788 |