Gap treatment

In the new variant of RNAalifold, gaps are not used for energy evaluations. That is before e.g. the energy of a hairpin loop is determined, all gaps are removed and the length of the hairpin used to compute the energy contribution is the length of the hairpin in the respective sequence.

Not using gaps in energy evaluations is especially useful if a vast majority of sequences of an alignment share these gaps, and only some have insertions that will lead to interior loops. An example of how the new treatment of gaps increases the predictive power of RNAalifold can be seen here:

• Prediction of GcvB structure

Covariance evaluation using RIBOSUM derived scores

Instead of using a hamming distance based score, we developed a scoring based on RIBOSUM matrices. There are two major atvantages of using these scores:

1. Scoring matrices can be chosen to fit to the alignment in question.
   In alignments with different pairwise identities the probabilitites to get consistent and compensatory mutations vary. The scoring can now reflect that.
   


2. In contrast to a hamming distance based score, having no mutations can also lead to a slight bonus.

Examples of how RIBOSUM based scores improve the predictive power of RNAalifold can be seen here:

• Prediction of Enterovirus 5' cloverleaf cis-acting replication element structure
• Prediction of Snake H/ACA box small nucleolar RNA structure
• Prediction of UnaL2 LINE 3' element structure

A combination of both effects can be seen in:

• R2 RNA element
• The Hammerhead Rybozyme