  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.
