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RNAPVMIN(1)			 User Commands			   RNAPVMIN(1)

NAME
       RNApvmin	- manual page for RNApvmin 2.4.18

SYNOPSIS
       RNApvmin	[options] _file.shape_

DESCRIPTION
       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 mini-
       mization	 process to calculate a	perturbation vector that minimizes the
       discripancies between predicted pairing probabilites and	observed pair-
       ing probabilities (deduced from given shape reactivities). Experimental
       data is read from a given SHAPE file and	normalized to  pairing	proba-
       bilities. The experimental data has to be provided in a multiline plain
       text file where each line has the format	'[position] [nucleotide]  [ab-
       solute 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 minimiza-
       tion has	terminated, the	obtained perturbation  vector  is  written  to
       stdout.

       -h, --help
	      Print help and exit

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

       --full-help
	      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.

       --shapeConversion=STRING
	      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 rep-
	      resents probabilities for	being unpaired rather than  raw	 reac-
	      tivity values

	      'L':  Use	a linear model to convert the reactivity into a	proba-
	      bility 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)

       --tauSigmaRatio=DOUBLE
	      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.

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

       --sampleSize=INT
	      The iterative minimization process requires to evaluate the gra-
	      dient 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 gra-
	      dient by sampling	the given number of sequences from the	ensem-
	      ble, which is much faster.

       -N, --nonRedundant
	      Enable non-redundant sampling strategy.  (default=off)

       --intermediatePath=STRING  Write	 an  output file for each iteration of
       the
	      minimization process.

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

       --initialVector=DOUBLE
	      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 re-
	      sults in a null vector. Every other value	x will be used to pop-
	      ulate  the  initial vector with random numbers from the interval
	      [-x,x].

       --minimizer=ENUM
	      Set the minimizing algorithm used	 for  finding  an  appropriate
	      perturbation  vector.   (possible	values="conjugate_fr", "conju-
	      gate_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 al-
	      gorithms implemented in the GNU Scientific Library. When the GNU
	      Scientific Library can not be found, only	the default  minimizer
	      is available.

       --initialStepSize=DOUBLE
	      The   initial   stepsize	 for   the  minimizer  methods.	  (de-
	      fault=`0.01')

       --minStepSize=DOUBLE
	      The  minimal  stepsize  for   the	  minizimer   methods.	  (de-
	      fault=`1e-15')

       --minImprovement=DOUBLE
	      The minimal improvement in the default minizimer method that has
	      to be surpassed to considered a new result a better  one.	  (de-
	      fault=`1e-3')

       --minimizerTolerance=DOUBLE
	      The tolerance to be used in the GSL minimizer

       methods.
	      (default=`1e-3')

   Model Details:
       -S, --pfScale=DOUBLE
	      Set  scaling factor for Boltzmann	factors	to prevent under/over-
	      flows.

	      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 dou-
	      ble precision (see the README file).

       -T, --temp=DOUBLE
	      Rescale energy parameters	to a  temperature  in  degrees	centi-
	      grade.  (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 dan-
	      gling 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	 inte-
	      rior 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 us-
	      ing this setting while partition function	uses -d2 setting, i.e.
	      dangling ends will be treated differently.

       --noLP 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 oc-
	      cur as helices of	length 1.

       --noGU Do not allow GU pairs.  (default=off)

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

       -P, --paramFile=paramfile
	      Read energy parameters from paramfile, instead of	using the  de-
	      fault parameter set.

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

       --nsp=STRING
	      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.

       --maxBPspan=INT
	      Set the maximum base pair	span.  (default=`-1')

REFERENCES
       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	Struc-
       tures", 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  thermodynam-
       ics   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 modifica-
       tion 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

EXAMPLES
       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 re-
       activities 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 so-
       lution was found, the calculated	perturbation vector will be  print  to
       stdout  and  can	then further be	used to	constrain RNAfold's MFE/parti-
       tion function calculation by applying the perturbation energies as soft
       constraints.

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

AUTHOR
       Dominik Luntzer,	Ronny Lorenz

REPORTING BUGS
       If  in doubt our	program	is right, nature is at fault.  Comments	should
       be sent to rna@tbi.univie.ac.at.

RNApvmin 2.4.18			  April	2021			   RNAPVMIN(1)

NAME | SYNOPSIS | DESCRIPTION | REFERENCES | EXAMPLES | AUTHOR | REPORTING BUGS

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