ArticleBioinformatics (Oxford, England)2019
Discovery of tandem and interspersed segmental duplications using high-throughput sequencing.
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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- SSLCNV: A Semi-supervised Learning Framework for Accurate Copy Number Variation Detection.Interdisciplinary sciences, computational life sciences · 2025Article
- SV-MeCa: an XGBoost-based meta-caller approach for structural variant calling from short-read data.BMC bioinformatics · 2025Article
- Genome-Wide Analysis ofPlants (Basel, Switzerland) · 2025Article
- Comparative study of tools for copy number variation detection using next-generation sequencing data.Scientific reports · 2025Article
- Comprehensive evaluation and guidance of structural variation detection tools in chicken whole genome sequence data.BMC genomics · 2024Article
- VISTA: an integrated framework for structural variant discovery.Briefings in bioinformatics · 2024Article
- DTDHM: detection of tandem duplications based on hybrid methods using next-generation sequencing data.PeerJ · 2024Article
- SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads.Nature methods · 2023Article
- Uncovering structural variants associated with body weight and obesity risk in labrador retrievers: a genome-wide study.Frontiers in genetics · 2023Article
- CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data.PLoS computational biology · 2022Article
- Genome-Scale Investigation ofInternational journal of molecular sciences · 2022Article
- Systematic analysis of CNGCs in cotton and the positive role of GhCNGC32 and GhCNGC35 in salt tolerance.BMC genomics · 2022Article
- Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants.Genomics, proteomics & bioinformatics · 2022Article
- svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network.BioMed research international · 2022Article
- Systematic analysis of CCCH zinc finger family in Brassica napus showed that BnRR-TZFs are involved in stress resistance.BMC plant biology · 2021Article
- Nebula: ultra-efficient mapping-free structural variant genotyper.Nucleic acids research · 2021Article
- Comparative genome analysis using sample-specific string detection in accurate long reads.Bioinformatics advances · 2021Article
- Towards a better understanding of the low recall of insertion variants with short-read based variant callers.BMC genomics · 2020Article
- Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.