Krannich, Thomas and White, W. Timothy J. and Niehus, Sebastian and Holley, Guillaume and Halldorsson, Bjarni and Kehr, Birte (2022) Population-scale detection of non-reference sequence variants using colored de Bruijn graphs. BIOINFORMATICS, 38 (3). pp. 604-611. ISSN 1367-4803, 1460-2059
Full text not available from this repository. (Request a copy)Abstract
Motivation: With the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared with other types of SVs due to the computational complexity of detecting them. When using shortread data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. Results: We introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENOMES; INSERTIONS; DIVERSITY; |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Zentren des Universitätsklinikums Regensburg > Regensburger Centrum für Interventionelle Immunologie (RCI) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 24 Aug 2022 14:20 |
| Last Modified: | 24 Aug 2022 14:20 |
| URI: | https://pred.uni-regensburg.de/id/eprint/46382 |
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