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Title: GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly.
Austin Authors: Cameron, Daniel L;Schröder, Jan;Penington, Jocelyn Sietsma;Do, Hongdo;Molania, Ramyar;Dobrovic, Alexander ;Speed, Terence P;Papenfuss, Anthony T
Affiliation: Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia
Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Department of Pathology, University of Melbourne, Parkville, Victoria, 3010, Australia
School of Cancer Medicine, La Trobe University, Bundoora, Victoria, 3084, Australia
Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre, Melbourne, Australia
Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia
Issue Date: Dec-2017 2017-11-02
Publication information: Genome research 2017; 27(12): 2050-2060
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.
DOI: 10.1101/gr.222109.117
ORCID: 0000-0002-0951-7116
PubMed URL: 29097403
Type: Journal Article
Appears in Collections:Journal articles

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