Dynamic time warping assessment of high-resolution melt curves provides a robust metric for fungal identification

Abstract

Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.

New approach to identify colistin‐resistant Pseudomonas aeruginosa by high‐resolution melting curve analysis assay

“…Some recent microbiological applications that used this technology have been reported. Various studies in Switzerland (Landolt et al ), USA (Esteves et al ), Italy (Pugliese et al ), and China (Lu et al ) demonstrated the potential of HRM to differentiate between bacterial species rapidly.…”

Mining of Behavioral Motifs from Individual Ant Trajectory

“…The DTW method is one of the methods used for analyzing time series data [1,[6][7][8] and can be adapted for trajectory shape analysis, as was done in this study. This study was successful in demonstrating the effectiveness of using the DTW method for fragment shape analysis.…”

“…To measure the similarity among fragmented trajectories for detecting unique classes of motifs, the DTW method was adopted. The DTW method can measure the similarity between 2 temporal sequences of data, such as shapes, genetic and sound analysis data [7,8], and trajectory similarity. The advantage of the DTW method is that it can align two temporal sequences automatically, as each trajectory typically has different time stamps.…”

“…In our previous study [4], we analyzed behavioral motifs of social insects using trajectories obtained from individual tracking in squared arenas. The well-established methods of BCPA (Behavioral Change Point Analysis, [5,6]) and DTW (Dynamic Time Warping, [7][8][9]) were adopted to obtain fragments of the trajectories and perform similarity calculations on them. However, the BCPA and DTW have calculation costs and require time to analyze the fragments of the trajectories.…”

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

“…DTW calculates the distance between reference data and test data which has never been trained, and the smallest distance indicates the greatest similarity [18][19][20]. And it holds the benefits of requiring small amount of fault reference data, no need of selecting feature vectors, and requiring limited historical data and a priori knowledge.…”