Fig. 1: Chromosight workflow and benchmark.

a Examples of distinct patterns visible on contact maps (loop, border and hairpin) and the corresponding chromosight kernels. b Matrix preprocessing involves normalisation balancing followed by the computation of observed/expected contacts. Only contacts between bins separated by a user-defined maximum distance are considered. The preprocessed matrix is then convolved with a kernel representing the pattern of interest. For each pixel of the matrix, a Pearson correlation coefficient is computed between the kernel and the surrounding window. A threshold is applied on the coefficients and a connected component labelling algorithm is used to separate groups of pixels (i.e. foci) with high correlation values. For each focus, the coordinates with the highest correlation value are used as the pattern coordinates. Coordinates located in poorly covered regions are discarded. c Comparison of Chromosight with different loop callers. Top: F1 score, Precision and Sensitivity scores assessed on labelled synthetic Hi–C data. Higher is better. d Run-time. e Memory usage according to maximum scanning distance and the amount of subsampled contact events, respectively. Means and standard deviations (grey areas) are plotted.