RNAplfold − manual page for RNAplfold 2.4.16
calculate locally stable secondary structure − pair probabilities
Computes local pair probabilities for base pairs with a maximal span of L. The probabilities are averaged over all windows of size L that contain the base pair. For a sequence of length n and a window size of L the algorithm uses only O(n+L*L) memory and O(n*L*L) CPU time. Thus it is practical to "scan" very large genomes for short stable RNA structures.
Output consists of a dot plot in postscript file, where the averaged pair probabilities can easily be parsed and visually inspected.
The -u option makes i possible to compute the probability that a stretch of x consequtive nucleotides is unpaired, which is useful for predicting possible binding sites. Again this probability is averaged over all windows containing the region.
WARNING! Output format changed!!
The output is a
plain text matrix containing on each line a position i
followed by the probability that i is unpaired, [i-1..i] is
unpaired [i-2..i] is unpaired and so on to the probability
that [i-x+1..i] is unpaired.
Print help and exit
Print help, including all details and hidden options, and exit
Print help, including hidden options, and exit
Print version and exit
Command line options which alter the general behavior of this program
Be verbose. (default=off)
Average the pair probabilities over windows of given size. (default=‘70’)
Set the maximum allowed separation of a base pair to span.
By setting the maximum base pair span no pairs (i,j) with j−i > span will be allowed. Defaults to winsize if parameter is omitted.
Report only base pairs with an average probability > cutoff in the dot plot. (default=‘0.01’)
Save memory by printing out everything during computation. (default=off)
NOTE: activated per default for sequences over 1M bp.
Compute the mean probability that regions of length 1 to a given length are unpaired. (default=‘31’)
Output is saved in a _lunp file.
Switch output from probabilities to their logarithms. (default=off)
This is NOT exactly the mean energies needed to unfold the respective stretch of bases! (implies −−ulength option).
Create additional output files for RNAplex. (default=off)
Do not automatically substitude nucleotide "T" with "U". (default=off)
Automatically generate an ID for each sequence. (default=off)
The default mode of RNAplfold is to automatically determine an ID from the input sequence data if the input file format allows to do that. Sequence IDs are usually given in the FASTA header of input sequences. If this flag is active, RNAplfold ignores any IDs retrieved from the input and automatically generates an ID for each sequence. This ID consists of a prefix and an increasing number. This flag can also be used to add a FASTA header to the output even if the input has none.
Prefix for automatically generated IDs (as used in output file names). (default=‘sequence’)
If this parameter is set, each sequences’ FASTA id will be prefixed with the provided string. FASTA ids then take the form ">prefix_xxxx" where xxxx is the sequence number. Hence, the output files will obey the following naming scheme: "prefix_xxxx_dp.ps" (dot−plot), "prefix_xxxx_lunp" (unpaired probabilities), etc. Note: Setting this parameter implies −−auto−id.
Change the delimiter between prefix and increasing number for automatically generated IDs (as used in output file names). (default=‘_’)
This parameter can be used to change the default delimiter "_" between
the prefix string and the increasing number for automatically generated ID.
Specify the number of digits of the counter in automatically generated alignment IDs. (default=‘4’)
When alignments IDs are automatically generated, they receive an increasing number, starting with 1. This number will always be left−padded by leading zeros, such that the number takes up a certain width. Using this parameter, the width can be specified to the users need. We allow numbers in the range [1:18]. This option implies −−auto−id.
Specify the first number in automatically generated alignment IDs. (default=‘1’)
When sequence IDs are automatically generated, they receive an increasing number, usually starting with 1. Using this parameter, the first number can be specified to the users requirements. Note: negative numbers are not allowed. Note: Setting this parameter implies to ignore any IDs retrieved from the input data, i.e. it activates the −−auto−id flag.
Change the delimiting character that is used for sanitized filenames. (default=‘ID−delimiter’)
This parameter can be used to change the delimiting character used while sanitizing filenames, i.e. replacing invalid characters. Note, that the default delimiter ALWAYS is the first character of the "ID delimiter" as supplied through the −−id−delim option. If the delimiter is a whitespace character or empty, invalid characters will be simply removed rather than substituted. Currently, we regard the following characters as illegal for use in filenames: backslash ’\’, slash ’/’, question mark ’?’, percent sign ’%’, asterisk ’*’, colon ’:’, pipe symbol ’|’, double quote ’"’, triangular brackets ’<’ and ’>’.
Use full FASTA header to create filenames. (default=off)
This parameter can be used to deactivate the default behavior of limiting output filenames to the first word of the sequence ID. Consider the following example: An input with FASTA header ">NM_0001 Homo Sapiens some gene" usually produces output files with the prefix "NM_0001" without the additional data available in the FASTA header, e.g. "NM_0001_dp.ps". With this flag set, no truncation of the output filenames is performed, i.e. output filenames receive the full FASTA header data as prefixes. Note, however, that invalid characters (such as whitespace) will be substituted by a delimiting character or simply removed, (see also the parameter option −−filename−delim).
Use SHAPE reactivity data to guide structure predictions.
Specify the method how to convert SHAPE reactivity data to pseudo energy contributions. (default=‘D’)
The following methods can be used to convert SHAPE reactivities into pseudo energy contributions.
’D’: Convert by using a linear equation according to Deigan et al 2009. The calculated pseudo energies will be applied for every nucleotide involved in a stacked pair. This method is recognized by a capital ’D’ in the provided parameter, i.e.: −−shapeMethod="D" is the default setting. The slope ’m’ and the intercept ’b’ can be set to a non−default value if necessary, otherwise m=1.8 and b=−0.6. To alter these parameters, e.g. m=1.9 and b=−0.7, use a parameter string like this: −−shapeMethod="Dm1.9b−0.7". You may also provide only one of the two parameters like: −−shapeMethod="Dm1.9" or −−shapeMethod="Db−0.7".
’Z’: Convert SHAPE reactivities to pseudo energies according to Zarringhalam et al 2012. SHAPE reactivities will be converted to pairing probabilities by using linear mapping. Aberration from the observed pairing probabilities will be penalized during the folding recursion. The magnitude of the penalties can affected by adjusting the factor beta (e.g. −−shapeMethod="Zb0.8").
’W’: Apply a given vector of perturbation energies to unpaired nucleotides according to Washietl et al 2012. Perturbation vectors can be calculated by using RNApvmin.
Specify the method used to convert SHAPE reactivities to pairing probabilities when using the SHAPE approach of Zarringhalam et al. (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.
’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)
Read additional commands from file.
Commands include hard and soft constraints, but also structure motifs in hairpin and interior loops that need to be treeted differently. Furthermore, commands can be set for unstructured and structured domains.
Rescale energy parameters to a temperature in degrees centigrade. (default=‘37.0’)
Do not include special tabulated stabilizing energies for tri−, tetra− and hexaloop hairpins. (default=off)
Mostly for testing.
Specify "dangling end" model for bases adjacent to helices in free ends and multi−loops. (default=‘2’)
With −d2 dangling energies will be added for the bases adjacent to a helix on both sides in any case while −d0 ignores dangling ends altogether (mostly for debugging).
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)
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.
Set scaling factor for Boltzmann factors to prevent under/overflows.
In the calculation of the partition function use pfScale * average_free_energy 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 longer folding windows.
Output accessibility profiles in binary format. (default=off)
The binary files produced by RNAplfold do not need to be parsed by RNAplex,
so that they are directly loaded into memory. This is useful when large sequences have to be searched for putative hybridization sites. Another advantage of the binary format is the 50% file size decrease.
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.
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 scaling of the Boltzmann factors. (default=‘1.’)
The argument provided with this option enables to scale the thermodynamic temperature used in the Boltzmann factors independently from the temperature used to scale the individual energy contributions of the loop types. The Boltzmann factors then become exp(−dG/(kT*betaScale)) where k is the Boltzmann constant, dG the free energy contribution of the state and T the absolute temperature.
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. H. Bernhart, U. Mueckstein, and I.L. Hofacker (2011), "RNA Accessibility in cubic time", Algorithms Mol Biol. 6: 3.
S. H. Bernhart, I.L. Hofacker, and P.F. Stadler (2006), "Local Base Pairing Probabilities in Large RNAs", Bioinformatics: 22, pp 614-615
A.F. Bompfuenewerer, R. Backofen, S.H. Bernhart, J. Hertel, I.L. Hofacker, P.F. Stadler, S. Will (2007), "Variations on RNA Folding and Alignment: Lessons from Benasque", J. Math. Biol.
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
Stephan H Bernhart, Ivo L Hofacker, Peter F Stadler, Ronny Lorenz
If in doubt our program is right, nature is at fault. Comments should be sent to firstname.lastname@example.org.