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To overcome this pitfall, multiple predication methods were used to reduce the false calls.
Three of the false calls were confirmed by MPP; the L858R false call was not confirmed by MPP.
MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples.
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While some of the difference in value could be explained by false base calls in our data set, it is doubtful that the false call rate would account for the magnitude of this difference.
The feasibility of identifying SNP markers for QTL fine mapping by high-throughput sequencing at a low sequencing depth depends on the false call rate, genome coverage, and sequencing cost.
With two of the six false calls resolved the assay delivered 188 correct calls out of 192 specimens tested, or an accuracy of 97.9%.
Nine (69%) out of the 13 false calls by Varscan 2 from the seven cell lines have alternate alleles in the normal samples.
The large number of false-positive sSNVs of these data allowed us to examine the typical false calls of these tools.
In the presence of local trends, the number of false calls tends to inflate and the penalty should thus be increased above the lower bound.
To reduce the amount of false calls based upon paralogy, each of the polymorphic sites and 70 flanking base pairs was extracted from the reference.
Precision then can be directly translated to the number of true called variants, or equivalently to the number of false calls, which supports quantitative, application-specific filter adjustment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com