Locating recombination hot spots in genomic sequences through the singular value decomposition

TL;DR

This paper introduces a fast, scalable method using singular value decomposition to locate recombination hotspots in genomic sequences, outperforming existing algorithms in synthetic and real HIV data.

Contribution

The authors develop a novel SVD-based approach for identifying recombination hotspots that is computationally efficient and effective compared to current methods.

Findings

Performs well on synthetic data scenarios

Successfully applied to HIV genomic data

Outperforms state-of-the-art algorithms

Abstract

Locating recombination hotspots in genomic data is an important but difficult task. Current methods frequently rely on estimating complicated models at high computational cost. In this paper we develop an extremely fast, scalable method for inferring recombination hot spots in a population of genomic sequences that is based on the singular value decomposition. Our method performs well in several synthetic data scenarios. We also apply our technique to a real data investigation of the evolution of drug therapy resistance in a population of HIV genomic sequences. Finally, we compare our method both on real and simulated data to a state of the art algorithm.