White, D. S. et al. Elife 9, e53357 (2020).

Statistically robust single-molecule experiments can include large numbers of long recordings of single-molecule trajectories. However, methods for analyzing these large datasets in an unsupervised manner have lagged methods for generating the data. White et al. have developed DISC (Divisive Segmentation and Clustering), which provides fast and accurate unsupervised analysis of large datasets. In DISC, model-free statistical learning is merged with the Viterbi algorithm, yielding analysis results that are more accurate than commonly used algorithms and improving speeds by three orders of magnitude. The researchers validated the performance of DISC on simulated data and then demonstrated the algorithm’s utility on experimental data looking at cooperativity of the binding of cAMP to cyclic nucleotide binding domains from cyclic nucleotide-gated ion channels.