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Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions
Genetic diseases are driven by aberrations of the human genome. Identification of such aberrations including structural variations (SVs) is key to our understanding. Conventional short-reads whole genome sequencing (cWGS) can identify SVs to base-pair resolution, but utilizes only short-range inform...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721175/ https://www.ncbi.nlm.nih.gov/pubmed/33226985 http://dx.doi.org/10.1371/journal.pcbi.1008397 |
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author | Sethi, Riccha Becker, Julia de Graaf, Jos Löwer, Martin Suchan, Martin Sahin, Ugur Weber, David |
author_facet | Sethi, Riccha Becker, Julia de Graaf, Jos Löwer, Martin Suchan, Martin Sahin, Ugur Weber, David |
author_sort | Sethi, Riccha |
collection | PubMed |
description | Genetic diseases are driven by aberrations of the human genome. Identification of such aberrations including structural variations (SVs) is key to our understanding. Conventional short-reads whole genome sequencing (cWGS) can identify SVs to base-pair resolution, but utilizes only short-range information and suffers from high false discovery rate (FDR). Linked-reads sequencing (10XWGS) utilizes long-range information by linkage of short-reads originating from the same large DNA molecule. This can mitigate alignment-based artefacts especially in repetitive regions and should enable better prediction of SVs. However, an unbiased evaluation of this technology is not available. In this study, we performed a comprehensive analysis of different types and sizes of SVs predicted by both the technologies and validated with an independent PCR based approach. The SVs commonly identified by both the technologies were highly specific, while validation rate dropped for uncommon events. A particularly high FDR was observed for SVs only found by 10XWGS. To improve FDR and sensitivity, statistical models for both the technologies were trained. Using our approach, we characterized SVs from the MCF7 cell line and a primary breast cancer tumor with high precision. This approach improves SV prediction and can therefore help in understanding the underlying genetics in various diseases. |
format | Online Article Text |
id | pubmed-7721175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77211752020-12-15 Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions Sethi, Riccha Becker, Julia de Graaf, Jos Löwer, Martin Suchan, Martin Sahin, Ugur Weber, David PLoS Comput Biol Research Article Genetic diseases are driven by aberrations of the human genome. Identification of such aberrations including structural variations (SVs) is key to our understanding. Conventional short-reads whole genome sequencing (cWGS) can identify SVs to base-pair resolution, but utilizes only short-range information and suffers from high false discovery rate (FDR). Linked-reads sequencing (10XWGS) utilizes long-range information by linkage of short-reads originating from the same large DNA molecule. This can mitigate alignment-based artefacts especially in repetitive regions and should enable better prediction of SVs. However, an unbiased evaluation of this technology is not available. In this study, we performed a comprehensive analysis of different types and sizes of SVs predicted by both the technologies and validated with an independent PCR based approach. The SVs commonly identified by both the technologies were highly specific, while validation rate dropped for uncommon events. A particularly high FDR was observed for SVs only found by 10XWGS. To improve FDR and sensitivity, statistical models for both the technologies were trained. Using our approach, we characterized SVs from the MCF7 cell line and a primary breast cancer tumor with high precision. This approach improves SV prediction and can therefore help in understanding the underlying genetics in various diseases. Public Library of Science 2020-11-23 /pmc/articles/PMC7721175/ /pubmed/33226985 http://dx.doi.org/10.1371/journal.pcbi.1008397 Text en © 2020 Sethi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sethi, Riccha Becker, Julia de Graaf, Jos Löwer, Martin Suchan, Martin Sahin, Ugur Weber, David Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title | Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title_full | Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title_fullStr | Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title_full_unstemmed | Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title_short | Integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
title_sort | integrative analysis of structural variations using short-reads and linked-reads yields highly specific and sensitive predictions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721175/ https://www.ncbi.nlm.nih.gov/pubmed/33226985 http://dx.doi.org/10.1371/journal.pcbi.1008397 |
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