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  • validation: Exome Sequencing data

    HI all,
    I have 10 pairs of normal-tumor whole exome data. The mutation calling has been performed and many interesting SNVs were detected. Allele Frequency cutoff 0.05 was used.

    However, our funding on this project is limited and it is unlikely to validate a large number of SNVs.

    We know that there are false positives in the obtained SNVs. I am thinking to use another tool to call the SNVs and use the common SNVs detected by both tools as the "true" SNVs. Then we can go ahead to calculate the mutation spectrum, mutually exclusivity ect.

    Here I am trying my luck to see if there is a better idea to get a relatively robust list of SNVs.

    Any input will be appreciated.

    Thank you.

  • #2
    Background Somatic mutation-calling based on DNA from matched tumor-normal patient samples is one of the key tasks carried by many cancer genome projects. One such large-scale project is The Cancer Genome Atlas (TCGA), which is now routinely compiling catalogs of somatic mutations from hundreds of paired tumor-normal DNA exome-sequence data. Nonetheless, mutation calling is still very challenging. TCGA benchmark studies revealed that even relatively recent mutation callers from major centers showed substantial discrepancies. Evaluation of the mutation callers or understanding the sources of discrepancies is not straightforward, since for most tumor studies, validation data based on independent whole-exome DNA sequencing is not available, only partial validation data for a selected (ascertained) subset of sites. Results To provide guidelines to comparing outputs from multiple callers, we have analyzed two sets of mutation-calling data from the TCGA benchmark studies and their partial validation data. Various aspects of the mutation-calling outputs were explored to characterize the discrepancies in detail. To assess the performances of multiple callers, we introduce four approaches utilizing the external sequence data to varying degrees, ranging from having independent DNA-seq pairs, RNA-seq for tumor samples only, the original exome-seq pairs only, or none of those. Conclusions Our analyses provide guidelines to visualizing and understanding the discrepancies among the outputs from multiple callers. Furthermore, applying the four evaluation approaches to the whole exome data, we illustrate the challenges and highlight the various circumstances that require extra caution in assessing the performances of multiple callers.


    Comparing somatic mutation-callers: beyond Venn diagrams
    Su Yeon Kim and Terence P Speed
    BMC Bioinformatics 2013, 14:189 doi:10.1186/1471-2105-14-189

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