Global Partition Function and Equilibrium Probabilities

Variations of the global partition function algorithm.

We provide implementations of the global partition function algorithm for

Single sequences,
Multiple sequence alignments (MSA), and
RNA-RNA hybrids

Basic global partition function interface

FLT_OR_DBL vrna_pf(vrna_fold_compound_t *fc, char *structure)

#include <ViennaRNA/part_func.h>

Compute the partition function \(Q\) for a given RNA sequence, or sequence alignment.

If structure is not a NULL pointer on input, it contains on return a string consisting of the letters ” . , | { } ( ) ” denoting bases that are essentially unpaired, weakly paired, strongly paired without preference, weakly upstream (downstream) paired, or strongly up- (down-)stream paired bases, respectively. If the model’s compute_bpp is set to 0 base pairing probabilities will not be computed (saving CPU time), otherwise after calculations took place pr will contain the probability that bases i and j pair.

SWIG Wrapper Notes:: This function is attached as method pf() to objects of type fold_compound. See, e.g. RNA.fold_compound.pf() in the Python API.

Note

This function is polymorphic. It accepts vrna_fold_compound_t of type VRNA_FC_TYPE_SINGLE, and VRNA_FC_TYPE_COMPARATIVE. Also, this function may return INF / 100. in case of contradicting constraints or numerical over-/underflow. In the latter case, a corresponding warning will be issued to stdout.

Parameters:

fc – [inout] The fold compound data structure
structure – [inout] A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)

Returns:

The ensemble free energy \(G = -RT \cdot \log(Q) \) in kcal/mol

vrna_dimer_pf_t vrna_pf_dimer(vrna_fold_compound_t *fc, char *structure)

#include <ViennaRNA/part_func.h>

Calculate partition function and base pair probabilities of nucleic acid/nucleic acid dimers.

This is the cofold partition function folding.

SWIG Wrapper Notes:: This function is attached as method pf_dimer() to objects of type fold_compound. See, e.g. RNA.fold_compound.pf_dimer() in the Python API.

See also

vrna_fold_compound() for how to retrieve the necessary data structure

Note

This function may return INF / 100. for the FA, FB, FAB, F0AB members of the output data structure in case of contradicting constraints or numerical over-/underflow. In the latter case, a corresponding warning will be issued to stdout.

Parameters:

fc – the fold compound data structure
structure – Will hold the structure or constraints

Returns:

vrna_dimer_pf_t structure containing a set of energies needed for concentration computations.

FLT_OR_DBL *vrna_pf_substrands(vrna_fold_compound_t *fc, size_t complex_size): #include <ViennaRNA/part_func.h>

FLT_OR_DBL vrna_pf_add(FLT_OR_DBL dG1, FLT_OR_DBL dG2, double kT): #include <ViennaRNA/part_func.h>

Simplified global partition function computation using sequence(s) or multiple sequence alignment(s)

float vrna_pf_fold(const char *sequence, char *structure, vrna_ep_t **pl)

#include <ViennaRNA/part_func.h>

Compute Partition function \(Q\) (and base pair probabilities) for an RNA sequence using a comparative method.

This simplified interface to vrna_pf() computes the partition function and, if required, base pair probabilities for an RNA sequence using default options. Memory required for dynamic programming (DP) matrices will be allocated and free’d on-the-fly. Hence, after return of this function, the recursively filled matrices are not available any more for any post-processing.

Note

In case you want to use the filled DP matrices for any subsequent post-processing step, or you require other conditions than specified by the default model details, use vrna_pf(), and the data structure vrna_fold_compound_t instead.

Parameters:

sequence – RNA sequence
structure – A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)
pl – A pointer to a list of vrna_ep_t to store pairing probabilities (Maybe NULL)

Returns:

The ensemble free energy \(G = -RT \cdot \log(Q) \) in kcal/mol

float vrna_pf_circfold(const char *sequence, char *structure, vrna_ep_t **pl)

#include <ViennaRNA/part_func.h>

Compute Partition function \(Q\) (and base pair probabilities) for a circular RNA sequences using a comparative method.

This simplified interface to vrna_pf() computes the partition function and, if required, base pair probabilities for a circular RNA sequence using default options. Memory required for dynamic programming (DP) matrices will be allocated and free’d on-the-fly. Hence, after return of this function, the recursively filled matrices are not available any more for any post-processing.

Folding of circular RNA sequences is handled as a post-processing step of the forward recursions. See Hofacker and Stadler [2006] for further details.

Note

In case you want to use the filled DP matrices for any subsequent post-processing step, or you require other conditions than specified by the default model details, use vrna_pf(), and the data structure vrna_fold_compound_t instead.

Parameters:

sequence – A circular RNA sequence
structure – A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)
pl – A pointer to a list of vrna_ep_t to store pairing probabilities (Maybe NULL)

Returns:

The ensemble free energy \(G = -RT \cdot \log(Q) \) in kcal/mol

float vrna_pf_alifold(const char **sequences, char *structure, vrna_ep_t **pl)

#include <ViennaRNA/part_func.h>

Compute Partition function \(Q\) (and base pair probabilities) for an RNA sequence alignment using a comparative method.

This simplified interface to vrna_pf() computes the partition function and, if required, base pair probabilities for an RNA sequence alignment using default options. Memory required for dynamic programming (DP) matrices will be allocated and free’d on-the-fly. Hence, after return of this function, the recursively filled matrices are not available any more for any post-processing.

Note

In case you want to use the filled DP matrices for any subsequent post-processing step, or you require other conditions than specified by the default model details, use vrna_pf(), and the data structure vrna_fold_compound_t instead.

Parameters:

sequences – RNA sequence alignment
structure – A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)
pl – A pointer to a list of vrna_ep_t to store pairing probabilities (Maybe NULL)

Returns:

The ensemble free energy \(G = -RT \cdot \log(Q) \) in kcal/mol

float vrna_pf_circalifold(const char **sequences, char *structure, vrna_ep_t **pl)

#include <ViennaRNA/part_func.h>

Compute Partition function \(Q\) (and base pair probabilities) for an alignment of circular RNA sequences using a comparative method.

This simplified interface to vrna_pf() computes the partition function and, if required, base pair probabilities for an RNA sequence alignment using default options. Memory required for dynamic programming (DP) matrices will be allocated and free’d on-the-fly. Hence, after return of this function, the recursively filled matrices are not available any more for any post-processing.

Folding of circular RNA sequences is handled as a post-processing step of the forward recursions. See Hofacker and Stadler [2006] for further details.

Note

In case you want to use the filled DP matrices for any subsequent post-processing step, or you require other conditions than specified by the default model details, use vrna_pf(), and the data structure vrna_fold_compound_t instead.

Parameters:

sequences – Sequence alignment of circular RNAs
structure – A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)
pl – A pointer to a list of vrna_ep_t to store pairing probabilities (Maybe NULL)

Returns:

The ensemble free energy \(G = -RT \cdot \log(Q) \) in kcal/mol

vrna_dimer_pf_t vrna_pf_co_fold(const char *seq, char *structure, vrna_ep_t **pl)

#include <ViennaRNA/part_func.h>

Calculate partition function and base pair probabilities of nucleic acid/nucleic acid dimers.

This simplified interface to vrna_pf_dimer() computes the partition function and, if required, base pair probabilities for an RNA-RNA interaction using default options. Memory required for dynamic programming (DP) matrices will be allocated and free’d on-the-fly. Hence, after return of this function, the recursively filled matrices are not available any more for any post-processing.

See also

vrna_pf_dimer()

Note

In case you want to use the filled DP matrices for any subsequent post-processing step, or you require other conditions than specified by the default model details, use vrna_pf_dimer(), and the data structure vrna_fold_compound_t instead.

Parameters:

seq – Two concatenated RNA sequences with a delimiting ‘&’ in between
structure – A pointer to the character array where position-wise pairing propensity will be stored. (Maybe NULL)
pl – A pointer to a list of vrna_ep_t to store pairing probabilities (Maybe NULL)

Returns:

vrna_dimer_pf_t structure containing a set of energies needed for concentration computations.

Functions

vrna_ep_t *vrna_plist_from_probs(vrna_fold_compound_t *fc, double cut_off)

#include <ViennaRNA/utils/structures.h>

Create a vrna_ep_t from base pair probability matrix.

The probability matrix provided via the vrna_fold_compound_t is parsed and all pair probabilities above the given threshold are used to create an entry in the plist

The end of the plist is marked by sequence positions i as well as j equal to 0. This condition should be used to stop looping over its entries

Parameters:

fc – [in] The fold compound
cut_off – [in] The cutoff value

Returns:

A pointer to the plist that is to be created

struct vrna_dimer_pf_s

#include <ViennaRNA/part_func.h>

Data structure returned by vrna_pf_dimer()

Public Members

double F0AB: Null model without DuplexInit.

double FAB: all states with DuplexInit correction

double FcAB: true hybrid states only

double FA: monomer A

double FB: monomer B

struct vrna_multimer_pf_s

Public Members

double F_connected: Fully connected ensemble (incl. DuplexInititiation and rotational symmetry correction.

double *F_monomers: monomers

size_t num_monomers: Number of monomers.