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A Two-Stage Algorithm for 3D Genome Reconstruction

Banff International Research Station for Mathematical Innovation and Discovery

The three-dimensional (3D) configuration of chromosomes within the eukaryote nucleus is consequential for several cellular functions including gene expression regulation and is also strongly associated with cancer-causing translocation events. While visualization of such architecture remains limited to low resolutions (due to compaction, dynamics and scale), the ability to infer structures at high resolution has been enabled by recently-devised chromosome conformation capture techniques. In particular, when coupled with next generation sequencing, such methods yield an unbiased inventory of genome-wide chromatin interactions. Various algorithms have been advanced to operate on such data to produce reconstructed 3D configurations. Several studies have shown that such reconstructions provide added value over raw interaction data with respect to downstream biological insights. However, such added value has yet to be fully realized for higher eukaryotes since no genome-wide reconstructions have been inferred for these organisms because of computational bottlenecks and organismal complexity. Here we propose a two-stage algorithm, deploying multi- dimensional scaling and Procrustes transformation, that overcomes these barriers. After showcasing 3D architectures for mouse embryonic stem cells and human lymphoblastoid cells we discuss methods for evaluating these solutions. Downstream deployment of 3D reconstructions to identify ""3D hotspots"" with respect to superposed functional data is also illustrated.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University of California, San Francisco

2016-04-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/57678

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/