Reconstructing DNA copy number by joint segmentation of multiple sequences

TL;DR

This paper introduces a new algorithm for detecting common copy number variations across multiple DNA sequences, offering computational efficiency and accuracy, aiding cancer research and genomic studies.

Contribution

The paper presents a novel regularization-based algorithm for joint segmentation of multiple sequences to identify shared DNA copy number variations.

Findings

Algorithm demonstrates competitive accuracy on simulated data.

Computational advantages over existing methods.

Effective on real cancer genome data.

Abstract

The variation in DNA copy number carries information on the modalities of genome evolution and misregulation of DNA replication in cancer cells; its study can be helpful to localize tumor suppressor genes, distinguish different populations of cancerous cell, as well identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand: this encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual. We present an algorithm based on regularization approaches with significant computational advantages and competitive accuracy. We illustrate its applicability with simulated and real data sets.