An integrative framework for the identification of double minute chromosomes using next generation sequencing data

Double minute chromosomes are circular fragments of DNA whose presence is associated with the onset of certain cancers. Double minutes are lethal, as they are highly amplified and typically contain oncogenes. Locating double minutes can supplement the process of cancer diagnosis, and it can help to identify therapeutic targets. However, there is currently a dearth of computational methods available to identify double minutes. We propose a computational framework for the idenfication of double minute chromosomes using next-generation sequencing data. Our framework integrates predictions from algorithms that detect DNA copy number variants, and it also integrates predictions from algorithms that locate genomic structural variants. This information is used by a graph-based algorithm to predict the presence of double minute chromosomes.

Using a previously published copy number variant algorithm and two structural variation prediction algorithms, we implemented our framework and tested it on a dataset consisting of simulated double minute chromosomes. Our approach uncovered double minutes with high accuracy, demonstrating its plausibility.

Although we only tested the framework with three programs (RDXplorer, BreakDancer, Delly), it can be extended to incorporate results from programs that 1) detect amplified copy number and from programs that 2) detect genomic structural variants like deletions, translocations, inversions, and tandem repeats. The software that implements the framework can be accessed here: https://github.com/mhayes20/DMFinder