DS_TECH769846 – Supplemental material for A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination

<p>Supplemental material, DS_TECH769846 for A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination by Mridu Sinha, Hannah Mack, Todd P. Coleman and Stephanie I. Fraley in SLAS Technology</p

Classification results with varied parameters.

<p>A) The KNN classifiers were tested by varying number of neighbors, k from 1 to 7. The plot shows average accuracy for each k. k = 1 and k = 2 resulted in the best performance. B) PCA-LDA classification result with varied number of eigenvectors. Our PCA-LDA classifiers were tested for dimensionality reduction varied from one through seven different eigenvectors. The plot shows the highest accuracy when using six eigenvectors.</p

Predicted melt curves of serotype 1 with the first primer set across 9 different conditions.

<p>The predicted melt curve were generated using uMelt with 9 different conditions, which are all combinations between [Na+ K+]: 47 mM, 50 mM, and 53 mM and [Mg2+]: 1.4 mM, 1.5 mM, and 1.6 mM.</p

Illustration of the ensemble binary classifiers.

<p>Each classifier would be used to differentiate two classes and the score will be count for each serotype. In a SVM classifier, each class consists of 9 melt curves from 9 different conditions. The result will be based on the serotype that returns the highest score.</p

Experimental melt curves from six different number of ‘CG’ sites DNA sequences.

<p>Melt curves of six synthetic DNA sequences from two duplicate experiments from different days. Different colors represent different sequences as legend. The fully methylated sequences represented in dark blue color with 10 ‘CG’ sites and then two ‘CG’ sites were changed to ‘TG’ to be the next target of 8 ‘CG’ sites and so on until all ‘CG’ sites were changed to ‘TG’ as 0 ‘CG’ sites (non-methylated) represented in light blue.</p

Average accuracy of the classifier under different Na+, K+ and Mg2+ concentrations.

<p>Average accuracy of the classifier under different Na+, K+ and Mg2+ concentrations.</p

Accuracy of different classifiers under different conditions.

<p>Horizontal axis shows the different Na+, K+ and Mg2+ concentrations respectively that were used to generate the predict curves. Vertical axis shows accuracy in %age. Different curves labeled with different legends represent the performance of different classifiers.</p