Primer design and marker clustering for multiplex SNP-IT primer extension genotyping assay using statistical modeling.

MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.

Auto-validation of fluorescent primer extension genotyping assay using signal clustering and neural networks.

BACKGROUND: SNP genotyping typically incorporates a review step to ensure that the genotype calls for a particular SNP are correct. For high-throughput genotyping, such as that provided by the GenomeLab SNPstream instrument from Beckman Coulter, Inc., the manual review used for low-volume genotyping becomes a major bottleneck. The work reported here describes the application of a neural network to automate the review of results. RESULTS: We describe an approach to reviewing the quality of primer extension 2-color fluorescent reactions by clustering optical signals obtained from multiple samples and a single reaction set-up. The method evaluates the quality of the signal clusters from the genotyping results. We developed 64 scores to measure the geometry and position of the signal clusters. The expected signal distribution was represented by a distribution of a 64-component parametric vector obtained by training the two-layer neural network onto a set of 10,968 manually reviewed 2D plots containing the signal clusters. CONCLUSION: The neural network approach described in this paper may be used with results from the GenomeLab SNPstream instrument for high-throughput SNP genotyping. The overall correlation with manual revision was 0.844. The approach can be applied to a quality review of results from other high-throughput fluorescent-based biochemical assays in a high-throughput mode.