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Biology subjects

Bryner, D.

Publications and source records attributed to Bryner, D..

2 recordsLinked to original sources

Towards A 3D Chromosome Shape Alphabet

The study of the 3-dimensional (3D) structure of chromosomes - the largest macromolecules in biology - is one of the most challenging to date in structural biology. Here, we develop a novel representation of chromosomes, as sequences of shape letters from a finite shape alphabet, which provides a compact and efficient way to analyze ensembles of chromosome shape data, akin to the analysis of texts in a language by using letters. We construct a Chromosome Shape Alphabet (CSA) from an ensemble of chromosome 3D structures inferred from Hi-C data - via SIMBA3D or other methods - by segmenting curves based on topologically associating domains (TADs) boundaries, and by clustering all TADs 3D structures into groups of similar shapes. The median shapes of these groups, with some pruning and processing, form the Chromosome Shape Letters (CSLs) of the alphabet. We provide a proof-of-concept for these CSLs by reconstructing independent test curves using only CSLs (and corresponding transformations) and comparing these reconstructions with the original curves. Finally, we demonstrate how CSLs can be used to summarize the variability of shapes in an ensemble of chromosome 3D structures using generalized sequence logos.

bioinformatics↗

Bayesian Estimation of 3D Chromosomal Structure from Single Cell Hi-C Data

The problem of 3D chromosome structure inference from Hi-C datasets is important and challenging. While bulk Hi-C datasets contain contact information derived from millions of cells, and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named SIMBA3D, to infer 3D structures of chromosomes from single cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C dataset as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

bioinformatics↗