PopIns: population-scale detection of novel sequence insertions

TL;DR

PopIns is a computational tool designed to detect and characterize novel sequence insertions of at least 100 base pairs across populations, improving accuracy and efficiency over previous methods using short read sequencing data.

Contribution

The paper introduces PopIns, a new method that combines assembly, merging, anchoring, and genotyping to detect population-scale novel insertions more accurately.

Findings

PopIns outperforms MindTheGap in recall and precision.

Merging contigs enhances insertion prediction quality.

Preliminary tests on 305 Icelanders demonstrate feasibility.

Abstract

The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. While the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions. We have developed the program PopIns, which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this paper, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a data set of 305 Icelanders demonstrate the practicality of the new approach. The source code of PopIns is available from http://github.com/bkehr/popins.