PCR, the polymerase chain reaction, is a fundamental tool of molecular biology. Quantitative PCR is the gold-standard methodology for determination of DNA copy numbers, quantitating transcription, and numerous other applications. A major barrier to large-scale application of PCR for quantitative genomic analyses is the current requirement for manual validation of individual PCR reactions to ensure generation of a single product. This typically requires visual inspection either of gel electrophoreses or temperature dissociation ("melting") curves of individual PCR reactions — a time-consuming and costly process.

Here we describe a robust computational solution to this fundamental problem. Using a training set of 10,080 reactions comprising multiple quantitative PCR reactions from each of 1,728 unique human genomic amplicons, we developed a support vector machine classifier capable of discriminating single-product PCR reactions with better than 99% accuracy. This approach has broad utility, and eliminates a major bottleneck to widespread application of PCR for high-throughput genomic applications.