Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.

Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applic...

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Main Authors: Tingting Gong, Vanessa M Hayes, Eva K F Chan
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0238108
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spelling doaj-f240b8579fd543b6a92b58c635d335c22021-03-03T22:02:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01158e023810810.1371/journal.pone.0238108Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.Tingting GongVanessa M HayesEva K F ChanSomatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user's guide and example use-cases.https://doi.org/10.1371/journal.pone.0238108
collection DOAJ
language English
format Article
sources DOAJ
author Tingting Gong
Vanessa M Hayes
Eva K F Chan
spellingShingle Tingting Gong
Vanessa M Hayes
Eva K F Chan
Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
PLoS ONE
author_facet Tingting Gong
Vanessa M Hayes
Eva K F Chan
author_sort Tingting Gong
title Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
title_short Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
title_full Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
title_fullStr Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
title_full_unstemmed Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.
title_sort shiny-sosv: a web-based performance calculator for somatic structural variant detection.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user's guide and example use-cases.
url https://doi.org/10.1371/journal.pone.0238108
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