Detailed Description

Collaboration diagram for Compute the Structure with Maximum Expected Accuracy (MEA):

Functions
char *	vrna_MEA (vrna_fold_compound_t fc, double gamma, float mea)
	Compute a MEA (maximum expected accuracy) structure. More...

char *	vrna_MEA_from_plist (vrna_ep_t plist, const char sequence, double gamma, vrna_md_t md, float mea)
	Compute a MEA (maximum expected accuracy) structure from a list of probabilities. More...

float	MEA (plist p, char structure, double gamma)
	Computes a MEA (maximum expected accuracy) structure. More...

Function Documentation

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.

Precondition: vrna_pf() must be executed on input parameter fc

Parameters

fc	The fold compound data structure with pre-filled 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

Returns: An MEA structure (or NULL on any error)

SWIG Wrapper Notes:: This function is attached as overloaded method MEA(gamma = 1.) to objects of type fold_compound. Note, that it returns the MEA structure and MEA value as a tuple (MEA_structure, MEA)

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.

Note: To include G-Quadruplex support, the corresponding field in md 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)

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)

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!

Detailed Description

Functions

Function Documentation