Read depth correction for somatic mutations

Jordan Anaya, Alexander S. Baras
2022 · doi:10.1101/2022.02.16.480761
preprint OA: closed CC-BY-4.0
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Abstract

ABSTRACT The ability to accurately detect mutations is a function of read depth and variant allele frequency (VAF). While the read depth distribution of a sample is observable, the true VAF distribution of all mutations in a sample is uncertain when there is low coverage depth. We propose to estimate the VAF distributions that would be observed with high-depth sequencing for samples with low sequencing depth by grouping samples with similar clonality and purity and using the VAF distributions observed with the high-depth mutations that are available. With these estimated high-depth VAF distributions we then calculate what the expected VAF distributions would be at a given depth and compare against the observed VAF distributions at that depth. Using this procedure we estimate that The Cancer Genome Atlas (TCGA) MC3 dataset only reports on average 83% of the mutations in a sample which would have been detected with high-depth sequencing. These results have important implications for comparing tumor mutational burden (TMB) estimates when samples are sequenced at different depths and for modeling high-depth, gene panel-based sequencing from the TCGA MC3 dataset.

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License: CC-BY-4.0