pseudoknots give better results than the heuristic pseudoknot energy parameters employed by the other algorithms. For example, the NeRNV and TMV 3 0 -UTRs both have five pseudoknots where four are simple H-type pseudoknots with interhelix loop 1 nt. For these pseudoknots, DotKnot gives the most accurate predictions, which we claim is due to the improved energy parameters by Cao and Chen (42, 48) . In terms of computational performance, DotKnot is very efficient due to the sparseness of the probability dot plot, the resulting low number of pseudoknot candidates and the implementation using dictionaries in Python. DotKnot runs in the order of seconds for all of the test sequences except T2 and T4, which take several minutes. For T4 with 1340 nt, we have 6567 candidate stems in dictionary D s and 7534 pseudoknot candidates before filtering. After the length-normalized filtering step, only 100 pseudoknot candidates remain for verification. Overall, it takes DotKnot <5 min to predict the correct pseudoknot in this sequence on our reference machine (Intel QC 2.66 GHz, 4 GB RAM). This is significantly faster than HotKnots, which takes 29 min, and pknotsRG, which takes 31 min. KnotSeeker is even faster than DotKnot and takes <2 min for the T4 sequence, because it does not rely on a partition function calculation. However, DotKnot is a more powerful prediction algorithm than For each pseudoknot, the best results in terms of both sensitivity S and positive predictive value PPV are marked in bold. The * symbol indicates that we were not able to run the algorithm due to the high time and space requirements. PK corresponds to the number of pseudoknots in the sequence as reported in the literature. We use pknots 1.05 with coaxial energies, pknotsRG 1.3 and HotKnots 1.2 without suboptimal solutions.