Fig. 1

The overview of SuperTAD pipeline. With the same input matrix, SuperTAD provides two modes for users. SuperTAD (the first mode) does not require any user-defined parameter and can determine the height of the coding tree by self-learning. SuperTAD(h) (the second mode) receives the manually selected h as the only parameter and finds the optimal coding tree with the constraint of h. For both modes, many coding tree candidates with various leaves number k are created. The optimal coding tree is selected by determining the most appropriate k. For SuperTAD, optional node filtering is performed to prune false-positive TADs from the optimal binary coding tree. The result after pruning is referred to as SuperTAD(F)