Compute the Structure with Maximum Expected Accuracy (MEA)
Functions

char *vrna_MEA(vrna_fold_compound_t *fc, double gamma, float *mea)
 #include <ViennaRNA/MEA.h>
Compute a MEA (maximum expected accuracy) structure.
The algorithm maximizes the expected accuracy
\[ A(S) = \sum_{(i,j) \in S} 2 \gamma p_{ij} + \sum_{i \notin S} p^u_i \]Higher values of \(\gamma\) result in more base pairs of lower probability and thus higher sensitivity. Low values of \(\gamma\) result in structures containing only highly likely pairs (high specificity). The code of the MEA function also demonstrates the use of sparse dynamic programming scheme to reduce the time and memory complexity of folding.
 SWIG Wrapper Notes:
This function is attached as overloaded method
MEA
(gamma = 1.) to objects of typefold_compound
. Note, that it returns the MEA structure and MEA value as a tuple (MEA_structure, MEA). See, e.g.RNA.fold_compound.MEA()
in the Python API.
 Parameters:
fc – The fold compound data structure with prefilled base pair probability matrix
gamma – The weighting factor for base pairs vs. unpaired nucleotides
mea – A pointer to a variable where the MEA value will be written to
 Pre:
vrna_pf() must be executed on input parameter
fc
 Returns:
An MEA structure (or NULL on any error)

char *vrna_MEA_from_plist(vrna_ep_t *plist, const char *sequence, double gamma, vrna_md_t *md, float *mea)
 #include <ViennaRNA/MEA.h>
Compute a MEA (maximum expected accuracy) structure from a list of probabilities.
The algorithm maximizes the expected accuracy
\[ A(S) = \sum_{(i,j) \in S} 2 \gamma p_{ij} + \sum_{i \notin S} p^u_i \]Higher values of \(\gamma\) result in more base pairs of lower probability and thus higher sensitivity. Low values of \(\gamma\) result in structures containing only highly likely pairs (high specificity). The code of the MEA function also demonstrates the use of sparse dynamic programming scheme to reduce the time and memory complexity of folding.
 SWIG Wrapper Notes:
This function is available as overloaded function
MEA_from_plist
(gamma = 1., md = NULL). Note, that it returns the MEA structure and MEA value as a tuple (MEA_structure, MEA). See, e.g.RNA.MEA_from_plist()
in the Python API.
Note
The unpaired probabilities \(p^u_i = 1  \sum_{j \neq i} p_{ij}\) are usually computed from the supplied pairing probabilities \(p_{ij}\) as stored in
plist
entries of type VRNA_PLIST_TYPE_BASEPAIR. To overwrite individual \(p^u_o\) values simply add entries with type VRNA_PLIST_TYPE_UNPAIRED To include GQuadruplex support, the corresponding field inmd
must be set. Parameters:
plist – A list of base pair probabilities the MEA structure is computed from
sequence – The RNA sequence that corresponds to the list of probability values
gamma – The weighting factor for base pairs vs. unpaired nucleotides
md – A model details data structure (maybe NULL)
mea – A pointer to a variable where the MEA value will be written to
 Returns:
An MEA structure (or NULL on any error)

float MEA(plist *p, char *structure, double gamma)
 #include <ViennaRNA/MEA.h>
Computes a MEA (maximum expected accuracy) structure.
The algorithm maximizes the expected accuracy
\[ A(S) = \sum_{(i,j) \in S} 2 \gamma p_{ij} + \sum_{i \notin S} p^u_i \]Higher values of \(\gamma\) result in more base pairs of lower probability and thus higher sensitivity. Low values of \(\gamma\) result in structures containing only highly likely pairs (high specificity). The code of the MEA function also demonstrates the use of sparse dynamic programming scheme to reduce the time and memory complexity of folding.
 Deprecated:
Use vrna_MEA() or vrna_MEA_from_plist() instead!

char *vrna_MEA(vrna_fold_compound_t *fc, double gamma, float *mea)