SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors.

(A) Schematic diagram of input data to SNVMix1. We show how allelic counts (bottom) are derived from aligned reads (top). The reference sequence is shown indicated in blue. The arrows indicate positions representing SNVs. The non-reference bases are shown in red. (B) Input data for SNVMix2 that consists of the mapping and base qualities. The darker the background for a read represents a higher quality alignment. The brighter colored nucleotides represent higher quality base calls. Therefore, high contrast nucleotides are more trustworthy than lower contrast nucleotides. (C) SNVMix1 shown as a probabilistic graphical model. Circles represent random variables, and rounded squares represent fixed constants. Shaded notes indicate observed data [the allelic counts and the read depth from (A)]. Unshaded nodes indicate quantities that are inferred during EM. Gi∈{aa, ab, bb} represents the genotype, Ni∈{0, 1,…,} is the number of reads and ai∈{0, 1,…, Ni} is the number of reference reads. π is the prior over genotypes and μk is the genotype-specific Binomial parameter for genotype k. (D) SNVMix2 shown as a probabilistic graphical model. In comparison to SNVMix1, ai is unobserved and we expand the input to consider read-specific information indexed by j where zij=1 indicates that read j is correctly aligned, qij is the base quality and rij is the mapping quality.

(A) Theoretical behavior of SNVmix at depths of 2, 3, 5, 10, 15, 20, 35, 50 and 100. The plots show how the distribution of marginal probabilities changes with the number of reference alleles given the model parameters fit to a 10× breast cancer genome dataset. (B) ROC curves from fitting SNVMix2 to synthetic data with increasing levels of certainty in the base call.

Conditional probability distributions of SNVMix model.

Distribution of AUC over 16 ovarian cancer transcriptomes comparing accuracy of SNV detection for two Maq runs, the best and worst SNVMix1 runs in the cross-validation experiment (middle) and best and worst runs for SNVMix2 (mbQ0 = no quality thresholding, MbQ30 = keeping only reads with mapping qualities > Q30). SNVMix1 and SNVMix2 runs were statistically more accurate than both Maq runs (ANOVA, P < 0.0001). SNVMix2 runs were better than SNVMix1, but not statistically significantly.