- Technical Note
- Open Access
PeakSeeker: a program for interpreting genotypes of mononucleotide repeats
© Thompson and Salipante et al; licensee BioMed Central Ltd. 2009
- Received: 15 December 2008
- Accepted: 03 February 2009
- Published: 03 February 2009
Mononucleotide repeat microsatellites are abundant, highly polymorphic DNA sequences, having the potential to serve as valuable genetic markers. Use of mononucleotide microsatellites has been limited by their tendency to produce "stutter", confounding signals from insertions and deletions within the mononucleotide tract that occur during PCR, which complicates interpretation of genotypes by masking the true position of alleles. Consequently, microsatellites with larger repeating subunits (dinucleotide and trinucleotide motifs) are used, which produce less stutter but are less genetically heterogeneous and less informative. A method to interpret the genotypes of mononucleotide repeats would permit the widespread use of those highly informative microsatellites in genetic research.
We have developed an approach to interpret genotypes of mononucleotide repeats using a software program, named PeakSeeker. PeakSeeker interprets experimental electropherograms as the most likely product of signals from individual alleles. Because mononucleotide tracts demonstrate locus-specific patterns of stutter peaks, this approach requires that the genotype pattern from a single allele is defined for each marker, which can be approximated by genotyping single DNA molecules or homozygotes. We have evaluated the program's ability to discriminate various types of homozygous and heterozygous mononucleotide loci using simulated and experimental data.
Mononucleotide tracts offer significant advantages over di- and tri-nucleotide microsatellite markers traditionally employed in genetic research. The PeakSeeker algorithm provides a high-throughput means to type mononucleotide tracts using conventional and widely implemented fragment length polymorphism genotyping. Furthermore, the PeakSeeker algorithm could potentially be adapted to improve, and perhaps to standardize, the analysis of conventional microsatellite genotypes.
- Root Mean Square Deviation
- Additive Product
- Mononucleotide Repeat
- Stutter Peak
- Simulated Genotype
Microsatellites are short (1- to 5-bp), tandemly repeated DNA motifs that are useful as genetic markers because they display a high degree of polymorphism within populations [1–3]. Polymorphisms consist of differences in the number of repeat sequences contained by a microsatellite and are the consequence of mutations which occur during DNA replication, when subunits are inserted or deleted . Although the mutation rate of microsatellites are influenced by a variety of factors [5, 6], they tend to be inversely proportional to the length of the repeat unit [7, 8]. Accordingly, mononucleotide repeats, uninterrupted tracts of A/T or G/C, are most susceptible to mutation  and are the most polymorphic  class of microsatellite. Polymorphisms at those sites are detectable even among somatic cells from the same individual [9, 10].
Stutter artifacts also complicate determining tract lengths by DNA sequencing , and even next-generation genomic sequencing technologies experience problems at mononucleotide runs . Although dedicated methods have been developed to detect single-base length differences in mononucleotide repeats, including mass-spectrometry  and primer-extension PCR [1, 23], none has come into common use.
The accessibility of highly informative mononucleotide microsatellites could be improved by a high-throughput means to detect single-base length polymorphisms using fragment length polymorphism genotyping, which is already in widespread use. Here we describe an approach to interpret the genotypes of mononucleotide repeats with the aid of an analysis algorithm, which we have named PeakSeeker.
PeakSeeker [Additional File 1: PeakSeeker_V1.zip] operates by interpreting the genotype of an autosomal mononucleotide repeat as the additive product of two homozygous genotypes, each corresponding to one of the two alleles. The program considers each homozygous and heterozygous combination of the two alleles' genotypes over the range where experimental data are present, and the relative amplification at each position is varied such that the additive product of the two allelic genotypes best fits the interrogated data. PeakSeeker scores each potential interpretation of the experimental genotype given how well the additive product fits the experimental data, and how realistic the required degree of unequal allelic amplification. The interpretation with the best score is reported, corresponding to the most probable interpretation. In order to reduce "noise" from stochastic variability between genotypes , the program can average together data from replicate genotypes and produce a "consensus" used for the analysis.
For full description of the program's workflow and scoring mechanism see [Additional File 2: MethodsSupplement.pdf].
(b) Simulated Genotypes
To simulate various polymorphisms, we combined peak patterns of single-molecule genotypes from mononucleotide microsatellites, which themselves well approximate the genotypes of single alleles. For four experimental loci, we superimposed genotypes of two simulated alleles differing in length from 0 to 4 bases, and simulated electropherograms representing the additive product of the alleles. Unequal amplification of alleles was modeled by varying the maximum height of each allele according to likelihoods from the unequal allelic amplification prior. To model the effects of novel insertion and deletion mutations occurring during PCR amplification, individual peaks in the genotypes of simulated alleles were allowed to vary in height from single-molecule genotypes by 2×(± 0.0143 (δ = 0.00679)) of the maximal peak's height, corresponding to the distribution of signal intensities expected from mutated PCR products . To represent variability introduced by the electrophoresis process, peaks were then modified by 2×(± 0.0052 (δ = 0.00744)) of the maximal peak's height . 100 simulations were produced for each of the four experimental loci, and the fraction of correct calls was calculated by the Maximum Likelihood Estimate, using the exact method for calculation of 95% confidence intervals.
(c) Experimental Data
Genomic DNA from ten passaged subclones of the NIH 3T3 (ATCC) cell line, previously reported , was genotyped [see Additional File 2: MethodsSupplement.pdf] at four tracts (Loci 188, 321, 502, and 1292) [see Additional File 3: Table 1.xls]. Six independent genotypes were produced for each locus/subclone pair. Proper genotype interpretation was established by manual analysis of genotypes based on the D-value metric, an arithmetic method of determining mononucleotide repeat heterozygosity or homozygosity based . Subsets of one to six of the replicates were randomly sampled and used as the basis for genotype interpretation by PeakSeeker. Summary statistics were calculated as before.
As a functional test, we genotyped ten passaged subclones from a diploid mouse cell line . To establish the proper genotype interpretation for each sample, we interrogated genotypes manually using an arithmetic method  unrelated to the PeakSeeker approach. Manual analysis revealed that four of the five microsatellites were polymorphic for multiple isolates, and that homozygous alleles and heterozygous alleles separated by differences of one base were represented. We then evaluated how frequently PeakSeeker correctly interpreted the electropherograms (Figure 4B). Again, the accuracy of PeakSeeker was proportional to the number of replicated genotypes used as the basis for data interpretation, although with lower accuracies than those obtained with simulated data, due to the presence of three sample/loci pairs which showed high rates of PCR error and were frequently called incorrectly. As before, locus 1292 yielded the highest accuracy, with 98.3% correct calls with only one replicate provided.
There were two instances where the results of PeakSeeker analysis and manual data analysis did not agree, but in both cases, PeakSeeker interpretation demonstrated that electropherograms were significantly different than expected from manual calls, and was therefore accepted as correct.
For the typical panel of mononucleotide microsatellites we examined here, PeakSeeker has proven well-suited to interpreting genotypes when overlap of alleles is the most significant, which are also the most difficult cases to call by eye. Thus, the program serves as a valuable augmentation to manual analysis, and can substantially increase throughput. However, if markers are selected for either limited stutter or asymmetric stutter peaks, the program should autonomously achieve perfect accuracy when a limited number of replicates are performed. The PeakSeeker algorithm could potentially be adapted to improve, and perhaps to standardize, the analysis of conventional microsatellites.
Project name: PeakSeeker v1.0
Project home page: None, program attached as supplementary information.
Operating system: Platform independent (tested on Linux and OS X)
Programming language: Perl, R Package for Statistics
Other requirements: GeneMapper v4.0 software (ABI) or equivalent, the R Project for Statistical Computing
License: Source code and executables are freely available for academic users.
Any restrictions to use by non-academics: License required
We thank Brian Schultz for his contributions to earlier drafts of the program, and Marshall Horwitz for his suggestions for the manuscript. JT supported by NIH T32GM07735, SJS supported by NIH F30AG030316, NIH DP1OD003278, NIH T32GM007266, and ARCS Fellowship grants to the University of Washington Medical Scientist Training Program.
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