Sensitivity and specificity of BAYSIC and other variant calling programs. A. Improvement of sensitivity and specificity of BAYSIC compared with input variant calling programs used with default parameters. SNP variants were detected jointly on ten samples from The 1000 Genomes Project using FreeBayes, SamTools, GATK (low quality filtered) and Atlas as in Figure 1, and the four variant sets and dbSNP were combined using BAYSIC. Sensitivity of each of the variant calling programs and BAYSIC was measured as the percent of SNPs confirmed by an orthogonal platform (SNP-chip) that was detected by the given program. Specificity was measured as the transition/transversion ratio (Ti/Tv) of all SNP variants called by each program. The sensitivity and specificity for SNPs in coding (top) and non-coding regions (bottom) are shown. Numbers accompanying black symbols indicate posterior probability cutoff used for generating the BAYSIC integrated variant sets. Horizontal dashed line indicates the specificity of the intersection of the four sets of variant predictions produced by FreeBayes, SamTools, GATK and Atlas. Vertical dashed line indicates sensitivity of the union of the four sets of variant predictions produced by FreeBayes, SamTools, GATK and Atlas. B. BAYSIC sensitivity and specificity compared with variant calling programs with continuous estimates of variant probability. Variants were detected using FreeBayes and GATK with varying stringency by applying cutoffs based on quality scores (for FreeBayes) or either Tranche scores or VQSLOD scores (for GATK). Sensitivity and specificity are shown for FreeBayes with cutoffs of Q10, Q20 (blue points) and GATK with Tranche cutoffs (open purple points, no cutoff, Tranche90, Tranche99 and Tranche99.9) or VQSLOD cutoffs (closed purple points, VQSLOD cutoffs of 0, 2.9, 4.4 or 5.4 from left to right). Sensitivity and specificity of BAYSIC using FreeBayes, Samtools, GATK and Atlas with default parameters as input are shown for comparison.