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GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly
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 IDentifi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741059/ https://www.ncbi.nlm.nih.gov/pubmed/29097403 http://dx.doi.org/10.1101/gr.222109.117 |
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author | Cameron, Daniel L. Schröder, Jan Penington, Jocelyn Sietsma Do, Hongdo Molania, Ramyar Dobrovic, Alexander Speed, Terence P. Papenfuss, Anthony T. |
author_facet | Cameron, Daniel L. Schröder, Jan Penington, Jocelyn Sietsma Do, Hongdo Molania, Ramyar Dobrovic, Alexander Speed, Terence P. Papenfuss, Anthony T. |
author_sort | Cameron, Daniel L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5741059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57410592018-01-23 GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly Cameron, Daniel L. Schröder, Jan Penington, Jocelyn Sietsma Do, Hongdo Molania, Ramyar Dobrovic, Alexander Speed, Terence P. Papenfuss, Anthony T. Genome Res Method 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. Cold Spring Harbor Laboratory Press 2017-12 /pmc/articles/PMC5741059/ /pubmed/29097403 http://dx.doi.org/10.1101/gr.222109.117 Text en © 2017 Cameron et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by/4.0/ 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/. |
spellingShingle | Method Cameron, Daniel L. Schröder, Jan Penington, Jocelyn Sietsma Do, Hongdo Molania, Ramyar Dobrovic, Alexander Speed, Terence P. Papenfuss, Anthony T. GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title | GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title_full | GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title_fullStr | GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title_full_unstemmed | GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title_short | GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly |
title_sort | gridss: sensitive and specific genomic rearrangement detection using positional de bruijn graph assembly |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741059/ https://www.ncbi.nlm.nih.gov/pubmed/29097403 http://dx.doi.org/10.1101/gr.222109.117 |
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