ArticleNucleic acids research2006
Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Relevance of Interleukins 6 and 8 Single Nucleotide Polymorphisms in Prostate Cancer: A Multicenter Study.Prostate cancer · 2021Article
- Repetitions of Strenuous Exercise Consistently Increase Paraoxonase 1 Concentration and Activity in Plasma of Average-Trained Men.Oxidative medicine and cellular longevity · 2021Article
- Article
- Replication of association of nine susceptibility loci with Graves' disease in the Chinese Han population.International journal of clinical and experimental medicine · 2014Article
- A base-calling algorithm for Tm-shifted melting curve SNP assay.Journal of clinical bioinformatics · 2011Article
- Identification of field caught Anopheles gambiae s.s. and Anopheles arabiensis by TaqMan single nucleotide polymorphism genotyping.Malaria journal · 2007Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single nucleotide polymorphisms (SNPs) are often determined using TaqMan real-time PCR assays (Applied Biosystems) and commercial software that assigns genotypes based on reporter probe signals at the end of amplification. Limitations to the large-scale application of this approach include the need for positive controls or operator intervention to set signal thresholds when one allele is rare. In the interest of optimizing real-time PCR genotyping, we developed an algorithm for automatic genotype calling based on the full course of real-time PCR data. Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay. The algorithm is unique in that it classifies samples according to the behavior of blanks (no DNA samples), which cluster with heterozygous samples. This method of classification eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare. Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing.
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