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

– Depiction of 2-color fluorescent readouts analyzed by the UHT Image™ software

<p><b>Copyright information:</b></p><p>Taken from "Auto-validation of fluorescent primer extension genotyping assay using signal clustering and neural networks"</p><p>BMC Bioinformatics 2004;5():36-36.</p><p>Published online 2 Apr 2004</p><p>PMCID:PMC406493.</p><p>Copyright © 2004 Huang et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.</p> Intensities from the two fluorescent channels presented in pseudo-colors are compared to determine genotypes. Three hundred eighty-four replicates of 4 × 4 tag arrays are produced on a single glass plate. Each 4 × 4 tag array has 4 control locations and 12 probe locations for 12 SNPs. The top left location is a positive control for both colors. The top right and bottom left locations are positive controls for the two different alleles, and the bottom right location is a negative control and has a probe that lacks a complementary tag sequence in the reaction. The controls are also used to mark the array boundaries for the image analysis software. – The UHTGetGenos software assigns genotype calls to individual SNP signal from every DNA sample. The results can be displayed as a P-plot (Figure ) by QCreview™ software for manual review (arrow to the right) or used to measure clustering parameters for auto-validation by the neural network (arrow down). – Schematic representation of SNP signal call clusters measured on the P-plot. The neural network uses 64 parameters described in to auto-classify P-plot as "Pass" or "Fail"