**RNAcop**

### About

*RNA Context Optimization by Probability* (RNAcop) is a tool for
optimizing lengths of flanking regions up- and downstream
of a constrained structure with respect to the probability of folding into the
structure. The motif is defined by constraints in
form of a dot bracket string. Starting from the first constraint in the 5′ end and
the last constraint in the 3′ end, RNAcop calculates the sequence with the highest
probability for observing the constrained structure by progressively adding
nucleotides to both flanking regions. Flanking regions are
extended from the input sequence. The constrained structure assumed to be part
of this input structure. Minimum and maximum lengths for each, the flanking
region in 5′ direction and 3′ direction, can be specified.

The probability for oberserving the structure motif is calculated by constrained folding implemented in the ViennaRNA package. More precisely, the partition function over all secondary structures satisfying the constraints is compared to the partition function over all possible secondary structures without constraints. The optimal probability is obtained using dynamic programming, i.e. two function calls are evaluated, one for constrained folding and one for folding without constraints. RNAcop prints the optimal sequence, suggested alternative suggestions, as well as the probability for the structure to be observed for all pair-wise combinations of flanking region extensions. The probability for observing a structure S given subsequence x is determined using the Boltzmann distribution of two ensembles of structures:

P(S|x) = e^{-( ΔΔG / (R*T) )}= Z

_{constrained}/ Z

_{no constraint}

where ΔΔG = ΔG_{constrained} - ΔG_{no constraint}

is the difference between the free energies associated with the two partition functions Z_{constrained} and Z_{no constraint}, respecitively, and ΔG = - RT ln Z.

The RNAcop webserver displays all pair-wise combinations of flanking regions
as log_{10} probability landscape plots. In addition, flanking regions
are suggested based on given minimum and maximum sizes for flanking regions
and a heuristic which selects suitable lengths of flanking regions by comparing
choices to the maximum probability inside the user-specified window of allowed
flanking region sizes. The heuristic selects flanking regions that differ
less than a specified log_{10} fold-change to the maximum probability
in the user-specified window area. Here, choices are preferred that allow flanking
regions to differ in size, but which still have a log_{10} fold-change
lower than the specified threshold. In other words, choices of flanking regions
that allow some tolerance both in 5′- and 3′ direction. For this
purpose, large square areas that satisfy the log_{10} fold-change
are identified.