Dataset Information

Confidence-based somatic mutation evaluation and prioritization.

ABSTRACT: 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.

SUBMITTER: Lower M

PROVIDER: S-EPMC3459886 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Confidence-based somatic mutation evaluation and prioritization.

Löwer Martin M Renard Bernhard Y BY de Graaf Jos J Wagner Meike M Paret Claudia C Kneip Christoph C Türeci Ozlem O Diken Mustafa M Britten Cedrik C Kreiter Sebastian S Koslowski Michael M Castle John C JC Sahin Ugur U

PLoS computational biology 20120927 9

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 confi ...[more]

PMID: 23028300

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Project description:ObjectiveThe clinical diagnosis of genetic disorders is undergoing transformation, driven by whole exome sequencing and whole genome sequencing (WES/WGS). However, such nucleotide-level resolution technologies create an interpretive challenge. Prior literature suggests that clinicians may employ characteristic cognitive processes during WES/WGS investigations to identify disruptions in genes causal for the observed disease. Based on cognitive ergonomics, we designed and evaluated a gene prioritization workflow that supported these cognitive processes.Materials and methodsWe designed a novel workflow in which clinicians recalled known genetic diseases with similarity to patient phenotypes to inform WES/WGS data interpretation. This prototype-based workflow was evaluated against the common computational approach based on physician-specified sets of individual patient phenotypes. The evaluation was conducted as a web-based user study, in which 18 clinicians analyzed 2 simulated patient scenarios using a randomly assigned workflow. Data analysis compared the 2 workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction.ResultsParticipants interpreted genetic diagnoses faster using prototype-based workflows. The 2 workflows did not differ in other evaluated aspects.DiscussionThe user study findings indicate that prototype-based approaches, which are designed to model experts' cognitive processes, can expedite gene prioritization and provide utility in synergy with common phenotype-driven variant/gene prioritization approaches. However, further research of the extent of this effect across diverse genetic diseases is required.ConclusionThe findings demonstrate potential for prototype-based phenotype description to accelerate computer-assisted variant/gene prioritization through complementation of skills and knowledge of clinical experts via human-computer interaction.

Dataset Information

Confidence-based somatic mutation evaluation and prioritization.

Publications

Confidence-based somatic mutation evaluation and prioritization.

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