1
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An Z, Jiang A, Chen J. Toward understanding the role of genomic repeat elements in neurodegenerative diseases. Neural Regen Res 2025; 20:646-659. [PMID: 38886931 PMCID: PMC11433896 DOI: 10.4103/nrr.nrr-d-23-01568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/21/2023] [Accepted: 03/02/2024] [Indexed: 06/20/2024] Open
Abstract
Neurodegenerative diseases cause great medical and economic burdens for both patients and society; however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.
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Affiliation(s)
- Zhengyu An
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Aidi Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Jingqi Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
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2
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Jiang T, Zhou Z, Zhang Z, Cao S, Wang Y, Liu Y. MEHunter: transformer-based mobile element variant detection from long reads. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae557. [PMID: 39287014 DOI: 10.1093/bioinformatics/btae557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 09/03/2024] [Accepted: 09/13/2024] [Indexed: 09/19/2024]
Abstract
SUMMARY Mobile genetic elements (MEs) are heritable mutagens that significantly contribute to genetic diseases. The advent of long-read sequencing technologies, capable of resolving large DNA fragments, offers promising prospects for the comprehensive detection of ME variants (MEVs). However, achieving high precision while maintaining recall performance remains challenging mainly brought by the variable length and similar content of MEV signatures, which are often obscured by the noise in long reads. Here, we propose MEHunter, a high-performance MEV detection approach utilizing a fine-tuned transformer model adept at identifying potential MEVs with fragmented features. Benchmark experiments on both simulated and real datasets demonstrate that MEHunter consistently achieves higher accuracy and sensitivity than the state-of-the-art tools. Furthermore, it is capable of detecting novel potentially individual-specific MEVs that have been overlooked in published population projects. AVAILABILITY AND IMPLEMENTATION MEHunter is available from https://github.com/120L021101/MEHunter.
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Affiliation(s)
- Tao Jiang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan 450000, China
| | - Zuji Zhou
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Zhendong Zhang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Shuqi Cao
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yadong Wang
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan 450000, China
| | - Yadong Liu
- Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan 450000, China
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3
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Zhang Z, Liu Y, Li X, Liu Y, Wang Y, Jiang T. HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data. Front Genet 2024; 15:1435087. [PMID: 39045321 PMCID: PMC11263161 DOI: 10.3389/fgene.2024.1435087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 06/13/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction: Structural Variants (SVs) are a type of variation that can significantly influence phenotypes and cause diseases. Thus, the accurate detection of SVs is a vital part of modern genetic analysis. The advent of long-read sequencing technology ushers in a new era of more accurate and comprehensive SV calling, and many tools have been developed to call SVs using long-read data. Haplotype-tagging is a procedure that can tag haplotype information on reads and can thus potentially improve the SV detection; nevertheless, few methods make use of this information. In this article, we introduce HapKled, a new SV detection tool that can accurately detect SVs from Oxford Nanopore Technologies (ONT) long-read alignment data. Methods: HapKled utilizes haplotype information underlying alignment data by conducting haplotype-tagging using Whatshap on the reads to improve the detection performance, with three unique calling mechanics including altering clustering conditions according to haplotype information of signatures, determination of similar SVs based on haplotype information, and slack filtering conditions based on haplotype quality. Results: In our evaluations, HapKled outperformed state-of-the-art tools and can deliver better SV detection results on both simulated and real sequencing data. The code and experiments of HapKled can be obtained from https://github.com/CoREse/HapKled. Discussion: With the superb SV detection performance that HapKled can deliver, HapKled could be useful in bioinformatics research, clinical diagnosis, and medical research and development.
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Affiliation(s)
- Zhendong Zhang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yue Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Xin Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yadong Liu
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, China
| | - Yadong Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, China
| | - Tao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, China
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4
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Kojima S. Investigating mobile element variations by statistical genetics. Hum Genome Var 2024; 11:23. [PMID: 38816353 PMCID: PMC11140006 DOI: 10.1038/s41439-024-00280-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/17/2024] [Accepted: 04/24/2024] [Indexed: 06/01/2024] Open
Abstract
The integration of structural variations (SVs) in statistical genetics provides an opportunity to understand the genetic factors influencing complex human traits and disease. Recent advances in long-read technology and variant calling methods for short reads have improved the accurate discovery and genotyping of SVs, enabling their use in expression quantitative trait loci (eQTL) analysis and genome-wide association studies (GWAS). Mobile elements are DNA sequences that insert themselves into various genome locations. Insertional polymorphisms of mobile elements between humans, called mobile element variations (MEVs), contribute to approximately 25% of human SVs. We recently developed a variant caller that can accurately identify and genotype MEVs from biobank-scale short-read whole-genome sequencing (WGS) datasets and integrate them into statistical genetics. The use of MEVs in eQTL analysis and GWAS has a minimal impact on the discovery of genome loci associated with gene expression and disease; most disease-associated haplotypes can be identified by single nucleotide variations (SNVs). On the other hand, it helps make hypotheses about causal variants or effector variants. Focusing on MEVs, we identified multiple MEVs that contribute to differential gene expression and one of them is a potential cause of skin disease, emphasizing the importance of the integration of MEVs in medical genetics. Here, I will provide an overview of MEVs, MEV calling from WGS, and the integration of MEVs in statistical genetics. Finally, I will discuss the unanswered questions about MEVs, such as rare variants.
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Affiliation(s)
- Shohei Kojima
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
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Ahsan MU, Liu Q, Perdomo JE, Fang L, Wang K. A survey of algorithms for the detection of genomic structural variants from long-read sequencing data. Nat Methods 2023; 20:1143-1158. [PMID: 37386186 PMCID: PMC11208083 DOI: 10.1038/s41592-023-01932-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/31/2023] [Indexed: 07/01/2023]
Abstract
As long-read sequencing technologies are becoming increasingly popular, a number of methods have been developed for the discovery and analysis of structural variants (SVs) from long reads. Long reads enable detection of SVs that could not be previously detected from short-read sequencing, but computational methods must adapt to the unique challenges and opportunities presented by long-read sequencing. Here, we summarize over 50 long-read-based methods for SV detection, genotyping and visualization, and discuss how new telomere-to-telomere genome assemblies and pangenome efforts can improve the accuracy and drive the development of SV callers in the future.
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Affiliation(s)
- Mian Umair Ahsan
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Qian Liu
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jonathan Elliot Perdomo
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA
| | - Li Fang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Ma H, Zhong C, Chen D, He H, Yang F. cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network. BMC Bioinformatics 2023; 24:119. [PMID: 36977976 PMCID: PMC10045035 DOI: 10.1186/s12859-023-05243-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants. RESULTS In this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV . CONCLUSIONS The proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage.
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Affiliation(s)
- Huidong Ma
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China.
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China.
| | - Danyang Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
| | - Haofa He
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
| | - Feng Yang
- School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China
- Key Laboratory of Parallel, Distributed and Intelligent Computing of Guangxi Universities and Colleges, Guangxi University, Nanning, 530004, China
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7
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Han S, Dias GB, Basting PJ, Viswanatha R, Perrimon N, Bergman C. Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line. Nucleic Acids Res 2022; 50:e124. [PMID: 36156149 PMCID: PMC9757076 DOI: 10.1093/nar/gkac794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/21/2022] [Accepted: 09/16/2022] [Indexed: 12/24/2022] Open
Abstract
Animal cell lines often undergo extreme genome restructuring events, including polyploidy and segmental aneuploidy that can impede de novo whole-genome assembly (WGA). In some species like Drosophila, cell lines also exhibit massive proliferation of transposable elements (TEs). To better understand the role of transposition during animal cell culture, we sequenced the genome of the tetraploid Drosophila S2R+ cell line using long-read and linked-read technologies. WGAs for S2R+ were highly fragmented and generated variable estimates of TE content across sequencing and assembly technologies. We therefore developed a novel WGA-independent bioinformatics method called TELR that identifies, locally assembles, and estimates allele frequency of TEs from long-read sequence data (https://github.com/bergmanlab/telr). Application of TELR to a ∼130x PacBio dataset for S2R+ revealed many haplotype-specific TE insertions that arose by transposition after initial cell line establishment and subsequent tetraploidization. Local assemblies from TELR also allowed phylogenetic analysis of paralogous TEs, which revealed that proliferation of TE families in vitro can be driven by single or multiple source lineages. Our work provides a model for the analysis of TEs in complex heterozygous or polyploid genomes that are recalcitrant to WGA and yields new insights into the mechanisms of genome evolution in animal cell culture.
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Affiliation(s)
| | | | - Preston J Basting
- Institute of Bioinformatics, University of Georgia, 120 E. Green St., Athens, GA, USA
| | - Raghuvir Viswanatha
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, USA
| | - Norbert Perrimon
- Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA, USA,Howard Hughes Medical Institute, Boston, MA, USA
| | - Casey M Bergman
- To whom correspondence should be addressed. Tel: +1 706 542 1764; Fax: +1 706 542 3910;
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8
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Fu Y, Mahmoud M, Muraliraman VV, Sedlazeck FJ, Treangen TJ. Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment. Gigascience 2021; 10:6375129. [PMID: 34561697 PMCID: PMC8463296 DOI: 10.1093/gigascience/giab063] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/22/2021] [Accepted: 08/29/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. FINDINGS We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. CONCLUSIONS Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.
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Affiliation(s)
- Yilei Fu
- Department of Computer Science, Rice University, Houston, TX 77251-1892, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Todd J Treangen
- Department of Computer Science, Rice University, Houston, TX 77251-1892, USA
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9
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Jiang T, Liu Y, Jiang Y, Li J, Gao Y, Cui Z, Liu Y, Liu B, Wang Y. Long-read-based human genomic structural variation detection with cuteSV. Genome Biol 2020; 21:189. [PMID: 32746918 PMCID: PMC7477834 DOI: 10.1186/s13059-020-02107-y] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 07/14/2020] [Indexed: 01/01/2023] Open
Abstract
Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.
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Affiliation(s)
- Tao Jiang
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yongzhuang Liu
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yue Jiang
- Nebula Genomics, Harbin, 150030, Heilongjiang, China
| | - Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, Guangdong, China
| | - Yan Gao
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Zhe Cui
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Yadong Liu
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China
| | - Bo Liu
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.
| | - Yadong Wang
- Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.
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10
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Abstract
Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.
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Affiliation(s)
- Steve S Ho
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alexander E Urban
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan E Mills
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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