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      GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

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          Abstract

          The identification of genomic rearrangements with high sensitivity and specificity using massively parallel sequencing remains a major challenge, particularly in precision medicine and cancer research. Here, we describe a new method for detecting rearrangements, GRIDSS (Genome Rearrangement IDentification Software Suite). GRIDSS is a multithreaded structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph-based assembler. By combining assembly, split read, and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line, and patient tumor data, recently winning SV subchallenge #5 of the ICGC-TCGA DREAM8.5 Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods while matching or exceeding their sensitivity. GRIDSS identifies nontemplate sequence insertions, microhomologies, and large imperfect homologies, estimates a quality score for each breakpoint, stratifies calls into high or low confidence, and supports multisample analysis.

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          Most cited references26

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          Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.

          : We describe Manta, a method to discover structural variants and indels from next generation sequencing data. Manta is optimized for rapid germline and somatic analysis, calling structural variants, medium-sized indels and large insertions on standard compute hardware in less than a tenth of the time that comparable methods require to identify only subsets of these variant types: for example NA12878 at 50× genomic coverage is analyzed in less than 20 min. Manta can discover and score variants based on supporting paired and split-read evidence, with scoring models optimized for germline analysis of diploid individuals and somatic analysis of tumor-normal sample pairs. Call quality is similar to or better than comparable methods, as determined by pedigree consistency of germline calls and comparison of somatic calls to COSMIC database variants. Manta consistently assembles a higher fraction of its calls to base-pair resolution, allowing for improved downstream annotation and analysis of clinical significance. We provide Manta as a community resource to facilitate practical and routine structural variant analysis in clinical and research sequencing scenarios.
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            DELLY: structural variant discovery by integrated paired-end and split-read analysis

            Motivation: The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome sequencing data. Of particular interest are integrated methods that accurately identify simple and complex rearrangements in heterogeneous sequencing datasets at single-nucleotide resolution, as an optimal basis for investigating the formation mechanisms and functional consequences of SVs. Results: We have developed an SV discovery method, called DELLY, that integrates short insert paired-ends, long-range mate-pairs and split-read alignments to accurately delineate genomic rearrangements at single-nucleotide resolution. DELLY is suitable for detecting copy-number variable deletion and tandem duplication events as well as balanced rearrangements such as inversions or reciprocal translocations. DELLY, thus, enables to ascertain the full spectrum of genomic rearrangements, including complex events. On simulated data, DELLY compares favorably to other SV prediction methods across a wide range of sequencing parameters. On real data, DELLY reliably uncovers SVs from the 1000 Genomes Project and cancer genomes, and validation experiments of randomly selected deletion loci show a high specificity. Availability: DELLY is available at www.korbel.embl.de/software.html Contact: tobias.rausch@embl.de
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              An integrated map of structural variation in 2,504 human genomes

              Summary Structural variants (SVs) are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight SV classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype-blocks in 26 human populations. Analyzing this set, we identify numerous gene-intersecting SVs exhibiting population stratification and describe naturally occurring homozygous gene knockouts suggesting the dispensability of a variety of human genes. We demonstrate that SVs are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of SV complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex SVs with multiple breakpoints likely formed through individual mutational events. Our catalog will enhance future studies into SV demography, functional impact and disease association.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                December 2017
                December 2017
                : 27
                : 12
                : 2050-2060
                Affiliations
                [1 ]Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia;
                [2 ]Department of Medical Biology, University of Melbourne, Parkville, Victoria, 3010, Australia;
                [3 ]Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, 3010, Australia;
                [4 ]Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, 3084, Australia;
                [5 ]Department of Pathology, University of Melbourne, Parkville, Victoria, 3010, Australia;
                [6 ]School of Cancer Medicine, La Trobe University, Bundoora, Victoria, 3084, Australia;
                [7 ]Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria, 3084, Australia;
                [8 ]Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, 3010, Australia;
                [9 ]Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre, Melbourne, 3000, Australia;
                [10 ]Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, 3010, Australia
                Author notes
                Corresponding author: papenfuss@ 123456wehi.edu.au
                Author information
                http://orcid.org/0000-0002-0951-7116
                http://orcid.org/0000-0001-5270-0605
                http://orcid.org/0000-0003-1561-0074
                http://orcid.org/0000-0003-3414-112X
                http://orcid.org/0000-0002-5403-7998
                http://orcid.org/0000-0002-1102-8506
                Article
                9509184
                10.1101/gr.222109.117
                5741059
                29097403
                c4f48f8d-baa1-436b-8fe0-de41e1c3af7d
                © 2017 Cameron et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 February 2017
                : 14 September 2017
                Page count
                Pages: 11
                Funding
                Funded by: Australian National Health and Medical Research Council (NHMRC) Program , open-funder-registry 10.13039/501100000925;
                Award ID: 1054618
                Funded by: NHMRC Senior Research Fellowship
                Award ID: 1116955
                Funded by: Australian Postgraduate Award
                Funded by: Victorian State Government Operational Infrastructure Support and Australian Government NHMRC Independent Research Institute Infrastructure Support
                Categories
                Method

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