Figure 4From: Predicting RNA secondary structure by the comparative approach: how to select the homologous sequencesCorrelation between SSCA scores (using the model M G C + G U MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae83mH00aaSbaaSqaaiab=zq8hjab=jq8djabgUcaRiab=zq8hjab=rr8vbqabaaaaa@3EE4@ ) and average MCC scores of homologous sequences of tmRNA (left) and RNaseP (right) alignments. Homologous sequences with the lowest SSCA scores have the highest average MCC scores. The best correlation is for the low SSCA scores.Back to article page