The screening and ranking algorithm to detect DNA copy number variations

TL;DR

This paper introduces SaRa, a fast and accurate algorithm for detecting DNA copy number variations with linear time complexity, suitable for high-throughput genomic data.

Contribution

The paper presents SaRa, a novel algorithm that reduces computational complexity to O(n) and provides theoretical analysis for CNV detection.

Findings

SaRa achieves faster detection compared to existing methods.

SaRa maintains high accuracy in CNV detection.

Theoretical properties of SaRa are well characterized.

Abstract

DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least $O(n^2)$, where $n$ is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.