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Bioinformatics. 2019 Apr 1. pii: btz237. doi: 10.1093/bioinformatics/btz237. [Epub ahead of print]

Discovery of tandem and interspersed segmental duplications using high throughput sequencing.

Soylev A1,2, Le T3,4, Amini H5, Alkan C1,6,7, Hormozdiari F3,8,9.

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Department of Computer Engineering, Bilkent University, Ankara, Turkey.
Department of Computer Engineering, Konya Food and Agriculture University, Konya, Turkey.
UC-Davis Genome Center, University of California, Davis, CA, USA.
Department of Computer Science, University of California, Davis, CA, USA.
Department of Neurology, School of Medicine, University of California, Davis, CA, USA.
Bilkent-Hacettepe Health Sciences and Technologies Program, Ankara, Turkey.
Department of Computer Science, ETH Zürich, Switzerland.
Department of Biochemistry and Molecular Medicine, University of California, Davis, CA, USA.
MIND Institute, University of California, Davis, CA, USA.



Several algorithms have been developed that use high throughput sequencing technology to characterize structural variations. Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions, and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.


We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing data sets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read.We evaluated the prediction performance of our algorithms through several experiments using both simulated and real data sets. In the simulation experiments, using a 30⨯ coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state of the art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (less than 5% for the top 50 predictions).


TARDIS source code is available at, and a corresponding Docker image is available at


Supplementary data are available at Bioinformatics online.

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