iSeg: an algorithm for segmentation of genomic data

TL;DR

iSeg is an efficient algorithm designed for segmentation of genomic data, accurately identifying significant regions by combining dynamic programming and tree-based data structures, outperforming existing methods on simulated and real datasets.

Contribution

The paper introduces iSeg, a novel segmentation algorithm that efficiently detects significant genomic regions using dynamic programming and data structures, improving over prior methods.

Findings

iSeg accurately identifies genomic segments with significant changes.

It outperforms existing segmentation methods on simulated datasets.

iSeg is effective on experimental genomic data.

Abstract

Identification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments differing from adjacent segments. In many applications, the mean of the measured values at multiple genomic locations in a segment is used to make inference of the property of interest. The segments with non-zero means often correspond to genomic regions with certain biological events, such as changes between two conditions. This problem is often called the segmentation problem in the field of genomics, and the change-point problem in other scientific disciplines. We designed an efficient algorithm, called iSeg, for segmentation of high-throughput genomic profiles. iSeg first utilizes dynamic programming to compute the significance for a large number of candidate segments. It then uses tree-based data structures to detect overlapping significant regions and update them simultaneously. Refinement and merging of significant segments are performed at the end to generate the final segmentation. We evaluate iSeg using both simulated and experimental datasets and show that it performs quite well when compared with existing methods.