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
; 2023.
Article
in English
| Web of Science | ID: covidwho-2311603
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 Frechet 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.
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
Journal of the Royal Statistical Society Series C-Applied Statistics
Year:
2023
Document Type:
Article
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