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  • detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

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    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1719
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
    background: driven by high throughput next generation sequencing technologies and the pressing need to deciphercancer genomes, computational approaches for detecting somatic single nucleotide variants (ssnvs) have undergonedramatic improvements during the past 2 years. the recently developed tools typically compare a tumor sampledirectly with a matched normal sample at each variant locus in order to increase the accuracy of ssnv calling. theseprograms also address the detection of ssnvs at low allele frequencies, allowing for the study of tumor heterogeneity,cancer subclones, and mutation evolution in cancer development. methods: we used whole genome sequencing (illumina genome analyzer iix platform) of a melanoma sample andmatched blood, whole exome sequencing (illumina hiseq 2000 platform) of 18 lung tumor-normal pairs and sevenlung cancer cell lines to evaluate six tools for ssnv detection: ebcall, jointsnvmix, mutect, somaticsniper, strelka, andvarscan 2, with a focus on mutect and varscan 2, two widely used publicly available software tools. default/suggestedparameters were used to run these tools. the missense ssnvs detected in these samples were validated through pcrand direct sequencing of genomic dna from the samples. we also simulated 10 tumor-normal pairs to explore theability of these programs to detect low allelic-frequency ssnvs. results: out of the 237 ssnvs successfully validated in our cancer samples, varscan 2 and mutect detected the mostof any tools (that is, 204 and 192, respectively). mutect identified 11 more low-coverage validated ssnvs than varscan2, but missed 11 more ssnvs with alternate alleles in normal samples than varscan 2. when examining the false calls ofeach tool using 169 invalidated ssnvs, we observed >63% false calls detected in the lung cancer cell lines had alternatealleles in normal samples. additionally, from our simulation data, varscan 2 identified more ssnvs than other tools,while mutect characterized most low allelic-fraction ssnvs. conclusions: our study explored the typical false-positive and false-negative detections that arise from the use ofssnv-calling tools. our results suggest that despite recent progress, these tools have significant room for improvement,especially in the discrimination of low coverage/allelic-frequency ssnvs and ssnvs with alternate alleles in normalsamples.

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