Confidence-based somatic mutation evaluation and prioritization.

Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54%...

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Autores principales:	Martin Löwer, Bernhard Y Renard, Jos de Graaf, Meike Wagner, Claudia Paret, Christoph Kneip, Ozlem Türeci, Mustafa Diken, Cedrik Britten, Sebastian Kreiter, Michael Koslowski, John C Castle, Ugur Sahin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1c52d2b15f6f4004b65dab08bbc818ce
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spelling	oai:doaj.org-article:1c52d2b15f6f4004b65dab08bbc818ce2021-11-18T05:50:57ZConfidence-based somatic mutation evaluation and prioritization.1553-734X1553-735810.1371/journal.pcbi.1002714https://doaj.org/article/1c52d2b15f6f4004b65dab08bbc818ce2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23028300/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.Martin LöwerBernhard Y RenardJos de GraafMeike WagnerClaudia ParetChristoph KneipOzlem TüreciMustafa DikenCedrik BrittenSebastian KreiterMichael KoslowskiJohn C CastleUgur SahinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 9, p e1002714 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Martin Löwer Bernhard Y Renard Jos de Graaf Meike Wagner Claudia Paret Christoph Kneip Ozlem Türeci Mustafa Diken Cedrik Britten Sebastian Kreiter Michael Koslowski John C Castle Ugur Sahin Confidence-based somatic mutation evaluation and prioritization.
description	Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.
format	article
author	Martin Löwer Bernhard Y Renard Jos de Graaf Meike Wagner Claudia Paret Christoph Kneip Ozlem Türeci Mustafa Diken Cedrik Britten Sebastian Kreiter Michael Koslowski John C Castle Ugur Sahin
author_facet	Martin Löwer Bernhard Y Renard Jos de Graaf Meike Wagner Claudia Paret Christoph Kneip Ozlem Türeci Mustafa Diken Cedrik Britten Sebastian Kreiter Michael Koslowski John C Castle Ugur Sahin
author_sort	Martin Löwer
title	Confidence-based somatic mutation evaluation and prioritization.
title_short	Confidence-based somatic mutation evaluation and prioritization.
title_full	Confidence-based somatic mutation evaluation and prioritization.
title_fullStr	Confidence-based somatic mutation evaluation and prioritization.
title_full_unstemmed	Confidence-based somatic mutation evaluation and prioritization.
title_sort	confidence-based somatic mutation evaluation and prioritization.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/1c52d2b15f6f4004b65dab08bbc818ce
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Confidence-based somatic mutation evaluation and prioritization.

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