Evidence map›Paper›PMID 30937433›Full record

ArticleBioinformatics (Oxford, England)2019

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

Arda Soylev, Thong Minh Le, Hajar Amini, Can Alkan, Fereydoun Hormozdiari

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. SSLCNV: A Semi-supervised Learning Framework for Accurate Copy Number Variation Detection.Interdisciplinary sciences, computational life sciences · 2025
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  11. Genome-Scale Investigation ofInternational journal of molecular sciences · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Arda SoylevDepartment of Computer Engineering, Bilkent University, Ankara.
Thong Minh LeUC-Davis Genome Center, University of California, Davis, CA, USA.
Hajar AminiDepartment of Neurology, School of Medicine, University of California, Davis, CA, USA.
Can AlkanDepartment of Computer Engineering, Bilkent University, Ankara.
Fereydoun HormozdiariUC-Davis Genome Center, University of California, Davis, CA, USA.

Funding

Mathematical and Computational Approaches in High-Throughput Genomics TrainingR25GM112625 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ESKIN, ELEAZAR · 2015 to 2019
$1.0M
NIGMS NIH HHS R25 GM112625
6 · The paper itself

Abstract

motivationSeveral algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). 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.

resultsWe developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. 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 datasets. 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 (<5% for the top 50 predictions). AVAILABILITY AND IMPLEMENTATION: TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

High-Throughput Nucleotide SequencingSegmental Duplications, GenomicAlgorithmsGenome, HumanGenomicsHumansSoftware

Identifiers

PMID30937433
PMCPMC6792081

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.