This figure displays a screenshot of MethMarker's biomarker performance comparison, assessing the robustness of candidate biomarkers to elevated error rates. In this example, CO_30 and CO_16 exhibit the overall best performance, in terms of low false positive/negative rates as well as high levels of accuracy, sensitivity, specificity and correlation.

MethMarker employs a six-step workflow to design, optimize and validate DNA methylation biomarkers for a given differentially methylated DNA region (DMR). In addition to its main purpose as a full-scale biomarker development tool, MethMarker can also be used simply as an assay design software, in which case steps 3 to 6 (yellow boxes) are omitted.

This figure shows a screenshot of MethMarker's main analysis window. From top to bottom, MethMarker displays gene annotation data for the region of interest; its genomic DNA sequence as well as the bisulfite converted sequence; automatically generated assays for the supported experimental methods (COBRA, bisulfite SNuPE, bisulfite pyrosequencing, MSP, MethyLight and MeDIP-qPCR); DNA methylation information for the region of interest, which has been loaded into MethMarker (yellow bars correspond to unmethylated CpGs, blue bars to methylated CpGs); a statistical summary of CpG positions within the region of interest; and - at the bottom - a text field providing advice for the user. All views are highly interactive and can be adjusted to control MethMarker's behavior.

A screenshot of MethMarker's performance window, summarizing the evaluation of a bisulfite pyrosequencing-based biomarker. In the upper panel, MethMarker displays the optimized regression formula, which predicts - based on measurement values for CpGs number 5 and 18 - whether a sample belongs to the case (that is, is methylated, indicated by positive score values) or to the control group (that is, is unmethylated, indicated by negative score values). Note that the score value is a measure of the probability with which the sample is a case rather than a control, not an estimate of the DNA methylation level (in fact, the probability p can be calculated from the score s with a simple formula: . The center panel displays the results of leave-one-out cross-validation, providing an estimate of the biomarker performance on new data. The diagrams at the bottom visualize the degree of separation between the two classes when plotting the measured level of DNA methylation over the score value of the regression formula (left) and the robustness of predictions in the face of increasing noise levels (right).