In our paper "Single-nucleotide polymorphisms and other mismatches reduce performance of quantitative PCR assays" (Clinical Chemistry, 2013 ), we've assessed the impact of mismatches in primer annealing sites on PCR efficiency. As expected, these results show a negative correlation between the number of mismatches and assay performance, with higher mismatch numbers resulting in higher Cq values and thus less generated product.
The same data can also be be used to in silico predict the specificity of an assay. When designing, the potential of each assay to generate aspecific or off-target products, can be assessed by aligning the primer sequences to the genome or transcriptome while allowing up to three mismatches per primer annealing site (and taking into account the maximum allowed product length). By collecting all possible annealing sites - both the perfect and imperfect (aspecific) ones - and tracking the position of mismatches in them, a score depicting the amplification potential of each site can be calculated. This enables the selection of the best assays - i.e. resulting in the least degree of aspecific product generation - during primer design and allows user to critically assess the quality of an assay before wet-lab testing.
Figure 1 : Percentage of assays with more than 98% of the reads mapping to on-target regions
Figure 2 : Percentage of assays showing more than 2% aspecific coverage, grouped by in silico specificity score. Numbers (x/y) in the bars indicate the number of assays with a certain with more than 2% non-specificity (x) across all assays having the corresponding in silico specificity score (y).
The validity of the above described workflow, which is implemented in pxlence' state-of-the-art primer design pipeline, was proven by experimentally testing almost 2,300 assays in two samples (for more details, see the paper in the knowledge center of our website). Following non-optimized PCR, generated products were amplified and the aspecific potential of each assay was determined by comparing on- and off-target sequencing coverage. Observations showed the majority of the assays (88%) to result in less than 2% off-target coverage (Figure 1). In addition, a good correlation could be shown between the calculated specificity score (ranging from 1 to 7, low to high degree of specificity) and the percentage of assays having more than 2% aspecific coverage (Figure 2), indicating that assays with higher specificity scores tend to result in less aspecific coverage. Together, these data illustrate once more the quality of our assays and the strength of our in silico specificity assessment workflow and primer design pipeline.