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.
| Original language | English |
|---|---|
| Article number | e56 |
| Journal | Nucleic Acids Research |
| Volume | 34 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2006 |
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ASJC Scopus subject areas
- Genetics
Cite this
Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data. / Callegaro, A.; Spinelli, R.; Beltrame, L.; Bicciato, S.; Caristina, L.; Censuales, S.; De Bellis, G.; Battaglia, Cristina.
In: Nucleic Acids Research, Vol. 34, No. 7, e56, 2006.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data
AU - Callegaro, A.
AU - Spinelli, R.
AU - Beltrame, L.
AU - Bicciato, S.
AU - Caristina, L.
AU - Censuales, S.
AU - De Bellis, G.
AU - Battaglia, Cristina
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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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U2 - 10.1093/nar/gkl185
DO - 10.1093/nar/gkl185
M3 - Article
C2 - 16617143
AN - SCOPUS:33645949715
VL - 34
JO - Nucleic Acids Research
JF - Nucleic Acids Research
SN - 0305-1048
IS - 7
M1 - e56
ER -