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

被引:235
作者
Cameron, Daniel L. [1 ,2 ]
Schroder, Jan [1 ,2 ,3 ]
Penington, Jocelyn Sietsma [1 ]
Do, Hongdo [4 ,5 ,6 ]
Molania, Ramyar [4 ,7 ]
Dobrovic, Alexander [4 ,5 ,6 ]
Speed, Terence P. [1 ,8 ]
Papenfuss, Anthony T. [1 ,2 ,8 ,9 ,10 ]
机构
[1] Walter & Eliza Hall Inst Med Res, Bioinformat Div, Parkville, Vic 3052, Australia
[2] Univ Melbourne, Dept Med Biol, Parkville, Vic 3010, Australia
[3] Univ Melbourne, Dept Comp & Informat Syst, Parkville, Vic 3010, Australia
[4] Olivia Newton John Canc Res Inst, Translat Genom & Epigen Lab, Heidelberg, Vic 3084, Australia
[5] Univ Melbourne, Dept Pathol, Parkville, Vic 3010, Australia
[6] La Trobe Univ, Sch Canc Med, Bundoora, Vic 3084, Australia
[7] Univ Melbourne, Austin Hlth, Dept Med, Heidelberg, Vic 3084, Australia
[8] Univ Melbourne, Dept Math & Stat, Parkville, Vic 3010, Australia
[9] Victorian Comprehens Canc Ctr, Peter MacCallum Canc Ctr, Melbourne, Vic 3000, Australia
[10] Univ Melbourne, Sir Peter MacCallum Dept Oncol, Parkville, Vic 3010, Australia
基金
英国医学研究理事会; 澳大利亚国家健康与医学研究理事会;
关键词
STRUCTURAL-VARIATION; PAIRED-END; VARIATION DISCOVERY; CANCER GENOMES; IDENTIFICATION; READS; INSERTIONS; RESOLUTION; VARIANTS; INDELS;
D O I
10.1101/gr.222109.117
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:2050 / 2060
页数:11
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