  In the recent decade, the emergence of liquid biopsy has significantly
improved cancer monitoring and detection. Dying cells, including those
originating from tumors, shed their DNA into the bloodstream and contribute to
a pool of circulating fragments called cell-free DNA (cfDNA). Identifying the
tissue origin of these DNA fragments from their epigenetic features has
implications in various clinical contexts. Open chromatin regions (OCRs) are
important epigenetic features of DNA that reflect cell types of origin.
Profiling these features by DNase-seq, ATAC-seq, and histone ChIP-seq provides
insights into tissue-specific and disease-specific regulatory mechanisms.
Integration of genomic and epigenomic features for cancer detection by liquid
biopsy has previously been reported. However, many multimodal analyses require
large amounts of cfDNA input and/or multiple types of experiments to cover the
genomic and epigenomic aspects of a single sample which is cost and time
prohibitive. Thus, methods that capture genomic and epigenomic profiles in a
single experiment type with low input requirements are of importance.
Predicting OCRs from whole genome sequencing (WGS) data is one such approach.
Here, we applied a correlation clustering algorithm to predict OCRs. We used
local sequencing depth as input to our algorithm. Multiple processing steps
were then applied as follows: count normalization, discrete Fourier transform
conversion, graph construction, graph cut optimization by linear programming,
and clustering. To validate the proposed method, we compared the output of our
predictions (OCR vs. non-OCR) with previously validated open chromatin regions
related to human blood samples of the ATAC-db. The percentage of overlap
between them is greater than 67%.

Genomics;Molecular Networks