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Nucleic Acids Research

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Conformational ensembles of biopolymers, whether proteins or chromosomes, can be described us- ing contact matrices. Principal component analysis (PCA) on the contact data has been used to in- terrogate both protein and chromosome structures and/or dynamics. However, as these fields have de- veloped separately, variants of PCA have emerged. Previously, a variant we hereby term Implicit-PCA (I- PCA) has been applied to chromosome contact ma- trices and revealed the spatial segregation of active and inactive chromatin. Separately, Explicit-PCA (E- PCA) has previously been applied to proteins and characterized their correlated structure fluctuations. Here, we swapped analysis methods (I-PCA and E- PCA), applying each to a different biopolymer type (chromosome or protein) than the one for which they were initially developed. We find that applying E-PCA to chromosome distance matrices derived from mi- croscopy data can reveal the dominant motion (con- certed fluctuation) of these chromosomes. Further, by applying E-PCA to Hi-C data across the human blood cell lineage, we isolated the aspects of chromo- some structure that most strongly differentiate cell types. Conversely, when we applied I-PCA to simula- tion snapshots of proteins, the major component re- ported the consensus features of the structure, mak- ing this a promising approach for future analysis of semi-structured proteins.


This article was published openly thanks to the University of Tennessee Open Publishing Support Fund.

Licensed under a Creative Commons Attribution 4.0 International license.

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