Abstract
Motivation Copy-number aberrations (CNA) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and subsequently a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of important CNAs.
Results We introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz yields more accurate segmentations relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that CNAViz’s more accurate segmentation improves accuracy in downstream copy-number calling.
Availability and implementation https://github.com/elkebir-group/cnaviz
Contact s.zaccaria{at}ucl.ac.uk, melkebir{at}illinois.edu
Competing Interest Statement
The authors have declared no competing interest.
