Cross validation set of the DNA samples of the <i>CYP2C9*2</i> assay.

<p>A. Normalized shifted derivative curves. Colors denote the called genotype (green: wild-type, red: homozygous mutant, blue: heterozygous mutant). Curves colored in gray are given a “no call” B. Corresponding 2D transformation scatter plot for visualization purposes. The three gray points in the space between the wild-type and homozygous mutant ellipses are the points that correspond to the “no-call” melt curves.</p

Separation of <i>MTHFR C</i>.<i>665C>T</i> genotypes using data from different temperature ranges.

<p>Left: 54 to 87C (probe and amplicon), middle: 54 to 74C (probe only) and right: 74 to 87C (amplicon only). Top: normalized derivative curves, middle: separation of genotype clusters in 2D. Bottom row shows the expected probability cross table via Monte Carlo simulation of 3D spherical coordinates.</p

Assay information including Target, amplicon size, and primer sequences.

<p>Assay information including Target, amplicon size, and primer sequences.</p

Automated Classification and Cluster Visualization of Genotypes Derived from High Resolution Melt Curves

<div><p>Introduction</p><p>High Resolution Melting (HRM) following PCR has been used to identify DNA genotypes. Fluorescent dyes bounded to double strand DNA lose their fluorescence with increasing temperature, yielding different signatures for different genotypes. Recent software tools have been made available to aid in the distinction of different genotypes, but they are not fully automated, used only for research purposes, or require some level of interaction or confirmation from an analyst.</p><p>Materials and Methods</p><p>We describe a fully automated machine learning software algorithm that classifies unknown genotypes. Dynamic melt curves are transformed to multidimensional clusters of points whereby a training set is used to establish the distribution of genotype clusters. Subsequently, probabilistic and statistical methods were used to classify the genotypes of unknown DNA samples on 4 different assays (40 <i>VKORC1</i>, <i>CYP2C9*2</i>, <i>CYP2C9*3</i> samples in triplicate, and 49 <i>MTHFR c</i>.<i>665C>T</i> samples in triplicate) run on the Roche LC480. Melt curves of each of the triplicates were genotyped separately.</p><p>Results</p><p>Automated genotyping called 100% of <i>VKORC1</i>, <i>CYP2C9*3</i> and MTHFR c.665C>T samples correctly. 97.5% of <i>CYP2C9*2</i> melt curves were genotyped correctly with the remaining 2.5% given a no call due to the inability to decipher 3 melt curves in close proximity as either homozygous mutant or wild-type with greater than 99.5% posterior probability.</p><p>Conclusions</p><p>We demonstrate the ability to fully automate DNA genotyping from HRM curves systematically and accurately without requiring any user interpretation or interaction with the data. Visualization of genotype clusters and quantification of the expected misclassification rate is also available to provide feedback to assay scientists and engineers as changes are made to the assay or instrument.</p></div

Automated genotyping procedure.

<p>A. Fluorescence (<i>F</i>) versus temperature (<i>T</i>). B.–<i>dF/dT</i> versus <i>T</i>. C. Temperature shifted–<i>dF/dT</i>. D. Normalized–<i>dF/dT</i> curves with training set genotype averages (black lines). E. A 3D point represents each curve correlated against each average curve. F. Points transformed to spherical coordinates. G. Genotype likelihood table H. 2D projection of correlation parameters for visualization.</p

Automated genotyping results.

<p>^a,b: For the CYP2C9*2 target assay, 1 wild-type sample and 2 homozygote samples were given no-calls due to maximum posterior probability values being less than 99.5%</p><p>Automated genotyping results.</p

T2 mapping images (A-F) and T2* mapping (A-F) images of rabbit cartilage at different time points after surgery (W2, W4, W8, W12, W16, and W20).

<p>The cartilage was integral and had moderate to slightly high linear homogeneous signal (A-B). The cartilage had moderate to slightly high signal and poor contrast with adjacent synovial fluid, adipose tissue and myeloid tissue(C-D). The cartilage was not integral and had slightly high heterogeneous signal(E-F).</p

The overview chart of the design in this study.

<p>MRI scanning was evaluated at different time points and histologic analysis was assessed at the same time (black arrows). ACLT = Anterior cruciate ligament transection. Control = Control group. n = 18 for ACLT and control group.</p

The scatter diagram of T2 values and Mankin scores.

<p>A positive correlation was found between the two variables (R² = 0.794, <i>P</i><0.001) using linear regression analysis. A correlation was also found between T2 and T2* values (<i>P</i><0.001). N = 18.</p

The scatter diagram of T2 values and T2* values.

<p>A correlation was found between T2 and T2* values (<i>P</i><0.001). N = 18.</p