Sequence-based multiscale modeling for high-throughput chromosome conformation capture (Hi-C) data analysis.

ABSTRACT: In this paper, we introduce sequence-based multiscale modeling for biomolecular data analysis. We employ spectral clustering method in our modeling and reveal the difference between sequence-based global scale clustering and local scale clustering. Essentially, two types of distances, i.e., Euclidean (or spatial) distance and genomic (or sequential) distance, can be used in data clustering. Clusters from sequence-based global scale models optimize spatial distances, meaning spatially adjacent loci are more likely to be assigned into the same cluster. Sequence-based local scale models, on the other hand, result in clusters that optimize genomic distances. That is to say, in these models, sequentially adjoining loci tend to be cluster together. We propose two sequence-based multiscale models

SUBMITTER: Xia K 

PROVIDER: S-EPMC5800693 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature