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RNApvmin − manual page for RNApvmin 2.4.18


RNApvmin [options] <file.shape>


RNApvmin 2.4.18

Calculate a perturbation vector that minimizes discripancies between predicted and observed pairing probabilities

The program reads a RNA sequence from stdin and uses an iterative minimization process to calculate a perturbation vector that minimizes the discripancies between predicted pairing probabilites and observed pairing probabilities (deduced from given shape reactivities). Experimental data is read from a given SHAPE file and normalized to pairing probabilities. The experimental data has to be provided in a multiline plain text file where each line has the format ’[position] [nucleotide] [absolute shape reactivity]’ (e.g. ’3 A 0.7’). The objective function used for the minimization may be weighted by choosing appropriate values for sigma and tau.

The minimization progress will be written to stderr. Once the minimization has terminated, the obtained perturbation vector is written to stdout.
, −−help

Print help and exit


Print help, including all details and hidden options, and exit


Print help, including hidden options, and exit

−V, −−version

Print version and exit

General Options:

Below are command line options which alter the general behavior of this program

−j, −−numThreads=INT

Set the number of threads used for calculations.


Specify the method used to convert SHAPE reactivities to pairing probabilities. (default=‘O’)

The following methods can be used to convert SHAPE reactivities into the probability for a certain nucleotide to be unpaired.

’M’: Use linear mapping according to Zarringhalam et al. 2012

’C’: Use a cutoff−approach to divide into paired and unpaired nucleotides (e.g. "C0.25")

’S’: Skip the normalizing step since the input data already represents probabilities for being unpaired rather than raw reactivity values

’L’: Use a linear model to convert the reactivity into a probability for being unpaired (e.g. "Ls0.68i0.2" to use a slope of 0.68 and an intercept of 0.2)

’O’: Use a linear model to convert the log of the reactivity into a probability for being unpaired (e.g. "Os1.6i−2.29" to use a slope of 1.6 and an intercept of −2.29)


Ratio of the weighting factors tau and sigma. (default=‘1.0’)

A high ratio will lead to a solution as close as possible to the experimental data, while a low ratio will lead to results close to the thermodynamic prediction without guiding pseudo energies.


The energies of the perturbation vector and the discripancies between predicted and observed pairing probabilities contribute to the objective function. This parameter defines, which function is used to process the contributions before summing them up. 0 square 1 absolute. (default=‘0’)


The iterative minimization process requires to evaluate the gradient of the objective function. (default=‘1000’)

A sample size of 0 leads to an analytical evaluation which scales as O(N^4). Choosing a sample size >0 estimates the gradient by sampling the given number of sequences from the ensemble, which is much faster.

−N, −−nonRedundant

Enable non−redundant sampling strategy. (default=off)

−−intermediatePath=STRING Write an output file for each iteration of

minimization process.

Each file contains the used perturbation vector and the score of the objective function. The number of the iteration will be appended to the given path.


Specify the vector of initial pertubations. (default=‘0’)

Defines the initial perturbation vector which will be used as starting vector for the minimization process. The value 0 results in a null vector. Every other value x will be used to populate the initial vector with random numbers from the interval [−x,x].


Set the minimizing algorithm used for finding an appropriate perturbation vector. (possible values="conjugate_fr", "conjugate_pr", "vector_bfgs", "vector_bfgs2", "steepest_descent", "default" default=‘default’)

The default option uses a custom implementation of the gradient descent algorithms while all other options represent various algorithms implemented in the GNU Scientific Library. When the GNU Scientific Library can not be found, only the default minimizer is available.


The initial stepsize for the minimizer methods. (default=‘0.01’)


The minimal stepsize for the minizimer methods. (default=‘1e−15’)


The minimal improvement in the default minizimer method that has to be surpassed to considered a new result a better one. (default=‘1e−3’)


The tolerance to be used in the GSL minimizer



Model Details:
, −−pfScale=DOUBLE

Set scaling factor for Boltzmann factors to prevent under/overflows.

In the calculation of the pf use scale*mfe as an estimate for the ensemble free energy (used to avoid overflows). The default is 1.07, useful values are 1.0 to 1.2. Occasionally needed for long sequences. You can also recompile the program to use double precision (see the README file).

−T, −−temp=DOUBLE

Rescale energy parameters to a temperature in degrees centigrade. (default=‘37.0’)

−4, −−noTetra

Do not include special tabulated stabilizing energies for tri−, tetra− and hexaloop hairpins. (default=off)

Mostly for testing.

−d, −−dangles=INT

Specify "dangling end" model for bases adjacent to helices in free ends and multi−loops. (default=‘2’)

With −d1 only unpaired bases can participate in at most one dangling end. With −d2 this check is ignored, dangling energies will be added for the bases adjacent to a helix on both sides in any case; this is the default for mfe and partition function folding (−p). The option −d0 ignores dangling ends altogether (mostly for debugging). With −d3 mfe folding will allow coaxial stacking of adjacent helices in multi−loops. At the moment the implementation will not allow coaxial stacking of the two interior pairs in a loop of degree 3 and works only for mfe folding.

Note that with −d1 and −d3 only the MFE computations will be using this setting while partition function uses −d2 setting, i.e. dangling ends will be treated differently.


Produce structures without lonely pairs (helices of length 1). (default=off)

For partition function folding this only disallows pairs that can only occur isolated. Other pairs may still occasionally occur as helices of length 1.


Do not allow GU pairs. (default=off)


Do not allow GU pairs at the end of helices. (default=off)

−P, −−paramFile=paramfile

Read energy parameters from paramfile, instead of using the default parameter set.

Different sets of energy parameters for RNA and DNA should accompany your distribution. See the RNAlib documentation for details on the file format. When passing the placeholder file name "DNA", DNA parameters are loaded without the need to actually specify any input file.


Allow other pairs in addition to the usual AU,GC,and GU pairs.

Its argument is a comma separated list of additionally allowed pairs. If the first character is a "−" then AB will imply that AB and BA are allowed pairs. e.g. RNAfold −nsp −GA will allow GA and AG pairs. Nonstandard pairs are given 0 stacking energy.

−e, −−energyModel=INT

Set energy model.

Rarely used option to fold sequences from the artificial ABCD... alphabet, where A pairs B, C−D etc. Use the energy parameters for GC (−e 1) or AU (−e 2) pairs.


Set the maximum base pair span. (default=‘−1’)


If you use this program in your work you might want to cite:

R. Lorenz, S.H. Bernhart, C. Hoener zu Siederdissen, H. Tafer, C. Flamm, P.F. Stadler and I.L. Hofacker (2011), "ViennaRNA Package 2.0", Algorithms for Molecular Biology: 6:26

I.L. Hofacker, W. Fontana, P.F. Stadler, S. Bonhoeffer, M. Tacker, P. Schuster (1994), "Fast Folding and Comparison of RNA Secondary Structures", Monatshefte f. Chemie: 125, pp 167-188

R. Lorenz, I.L. Hofacker, P.F. Stadler (2016), "RNA folding with hard and soft constraints", Algorithms for Molecular Biology 11:1 pp 1-13

S. Washietl, I.L. Hofacker, P.F. Stadler, M. Kellis (2012) "RNA folding with soft constraints: reconciliation of probing data and thermodynamics secondary structure prediction" Nucl Acids Res: 40(10), pp 4261-4272

The energy parameters are taken from:

D.H. Mathews, M.D. Disney, D. Matthew, J.L. Childs, S.J. Schroeder, J. Susan, M. Zuker, D.H. Turner (2004), "Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure", Proc. Natl. Acad. Sci. USA: 101, pp 7287-7292

D.H Turner, D.H. Mathews (2009), "NNDB: The nearest neighbor parameter database for predicting stability of nucleic acid secondary structure", Nucleic Acids Research: 38, pp 280-282


RNApvmin acceptes a SHAPE file and a corresponding nucleotide sequence, which is read form stdin.

  RNApvmin sequence.shape < sequence.fasta > sequence.pv

The normalized SHAPE reactivity data has to be stored in a text file, where each line contains the position and the reactivity for a certain nucleotide ([position] [nucleotide] [SHAPE reactivity]).

  1 A 1.286
  2 U 0.383
  3 C 0.033
  4 C 0.017
  98 U 0.234
  99 G 0.885

The nucleotide information in the SHAPE file is optional and will be used to cross check the given input sequence if present. If SHAPE reactivities could not be determined for every nucleotide, missing values can simply be omited.

The progress of the minimization will be printed to stderr. Once a solution was found, the calculated perturbation vector will be print to stdout and can then further be used to constrain RNAfold’s MFE/partition function calculation by applying the perturbation energies as soft constraints.

  RNAfold --shape=sequence.pv --shapeMethod=W < sequence.fasta


Dominik Luntzer, Ronny Lorenz


If in doubt our program is right, nature is at fault. Comments should be sent to