KnotInFrame: prediction of -1 ribosomal frameshift events

Theis, Corinna; Reeder, Jens; Giegerich, Robert

Next, we have a closer look at the normalized dominance of the annotated pseudoknots and in particular the false negative (FN) cases, where KnotInFrame does not find the annotated positions or assigns to them a low rank. Figure 2 . A diagram of the prediction pipeline KnotInFrame. In the search phase, a set of input sequences is searched for consensus slippery motifs. Discarding untranslatable sites by means of the first filter ISF and folding the remaining candidates with pknotsRG-fs and RNAfold flows into the filter phase. Three filters (LEF, EDF and NDF) discard further slippery sites based on the candidates' energy values. In a last phase rank the candidates will be ranked according to their normalized energy dominance. From Table 2 , one can observe, that 20 of 26 real pseudoknots have a Á > 0. Sixteen of these 20 have also been predicted with our pipeline on rank 1. This, of course, confirms that a positive Á is a strong indicator for frameshift sites. From our small RECODE test set it is hard to conclude a definite threshold for a good separation of strong and weak candidates. In Figure 3 , we show that in fact true PRF signals tend to have a higher Á than nonshifting slippery sites. However, the separation of the distributions is by no means sufficient for a clear classification. As a rule of thumb, we can state that candidates with Á ! 0:1 are most likely true signals. With Á ! 0:05, we capture approximately the same number of false positive (FPs) and TPs. Finding the appropriate balance between high sensitivity and selectivity for this problem should therefore be governed by the intended use of the program. We also note that there are annotated pseudoknots with a Á 0. These are also the candidates where KnotInFrame failed to rank the true À1 PRF signal on a high position. We examined those FN in more detail and give explanations for their mispredictions: