RNAsubopt − manual page for RNAsubopt 2.4.18
calculate suboptimal secondary structures of RNAs
Reads RNA sequences from stdin and (in the default −e mode) calculates all suboptimal secondary structures within a user defined energy range above the minimum free energy (mfe). It prints the suboptimal structures in dot−bracket notation followed by the energy in kcal/mol to stdout. Be careful, the number of structures returned grows exponentially with both sequence length and energy range.
when used with the −p option, RNAsubopt
produces Boltzmann weighted samples of secondary structures.
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)
Do not automatically substitude nucleotide "T" with "U". (default=off)
Read a file instead of reading from stdin.
The default behavior of RNAsubopt is to read input from stdin. Using this parameter the user can specify an input file name where data is read from.
Print output to file instead of stdout.
This option may be used to write all output to output files rather than printing to stdout. The default filename is "RNAsubopt_output.sub" if no FASTA header precedes the input sequences and the −−auto−id feature is inactive. Otherwise, output files with the scheme "prefix.sub" are generated, where the "prefix" is taken from the sequence id. The user may specify a single output file name for all data generated from the input by supplying an optional string as argument to this parameter. In case a file with the same filename already exists, any output of the program will be appended to it. Note: Any special characters in the filename will be replaced by the filename delimiter, hence there is no way to pass an entire directory path through this option yet. (See also the "−−filename−delim" parameter)
Automatically generate an ID for each sequence. (default=off)
The default mode of RNAsubopt 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, RNAsubopt 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. 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.sub". 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).
Command line options to interact with the structure constraints feature of this program
Set the maximum base pair span. (default=‘−1’)
−C, −−constraint[=<filename>] Apply structural constraint(s) during
The program first reads the sequence(s), then a dot−bracket like string containing constraints on the structure. The following symbols are recognized:
’.’ ... no constraint for this base
’x’ ... the base is unpaired
’<’ ... the base pairs downstream, i.e. i is paired with j > i
’>’ ... the base pairs upstream, i.e. i is paired with j < i
’|’ ... the corresponding base has to be paired
’()’ ... base i pairs with base j
Due to historic behavior of this program, all pairing constraints will only
disallow pairs that conflict with the constraint. This is usually sufficient to enforce the constraint, but occasionally a base may stay unpaired in spite of constraints. Use the −−enforceConstraint to really exclude unpaired states.
Use constraints for multiple sequences. (default=off)
Usually, constraints provided from input file only apply to a single input sequence. Therefore, RNAsubopt will stop its computation and quit after the first input sequence was processed. Using this switch, RNAsubopt processes multiple input sequences and applies the same provided constraints to each of them.
Remove non−canonical base pairs from the structure constraint. (default=off)
Enforce base pairs given by round brackets ( ) in structure constraint. (default=off)
Use SHAPE reactivity data in the folding recursions (does not work for Zuker suboptimals).
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.
Select the algorithms which should be applied to the given RNA sequence.
Compute suboptimal structures with energy in a certain range of the optimum (kcal/mol).
Default is calculation of mfe structure only.
Only print structures with energy within range of the mfe after post reevaluation of energies.
Useful in conjunction with −logML, −d1 or −d3: while the −e option specifies the range before energies are re−evaluated, this option specifies the maximum energy after re−evaluation.
Sort the suboptimal structures by energy and lexicographical order. (default=off)
Structures are first sorted by energy in ascending order. Within groups of the same energy, structures are then sorted in ascending in lexicographical order of their dot−bracket notation. See the −−en−only flag to deactivate this second step. Note that sorting is done in memory, thus it can easily lead to exhaution of RAM! This is especially true if the number of structures produced becomes large or the RNA sequence is rather long. In such cases better use an external sort method, such as UNIX "sort".
Only sort structures by free energy. (default=off)
In combination with −−sorted, this flag deactivates the second sorting criteria and sorts structures solely by their free energy instead of additionally sorting by lexicographic order in each energy band. This might save some time during the sorting process in situations where lexicographic order is not required.
Randomly draw structures according to their probability in the Boltzmann ensemble.
Instead of producing all suboptimals in an energy range, produce a random sample of suboptimal structures, drawn with probabilities equal to their Boltzmann weights via stochastic backtracking in the partition function. The −e and −p options are mutually exclusive.
Same as "−−stochBT" but also print free energies and probabilities of the backtraced structures.
Enable non−redundant sampling strategy. (default=off)
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).
Assume a circular (instead of linear) RNA molecule. (default=off)
Compute density of states instead of secondary structures. (default=off)
This option enables the evaluation of the number of secondary structures in certain energy bands around the MFE.
Compute Zuker suboptimals instead of all suboptimal structures within an energy band around the MFE. (default=off)
Incoorporate G−Quadruplex formation. (default=off)
No support of G−quadruplex prediction for stochastic backtracking and Zuker−style suboptimals yet).
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 −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)
Recompute energies of structures using a logarithmic energy function for multi−loops before output. (default=off)
This option does not effect structure generation, only the energies that are printed out. Since logML lowers energies somewhat, some structures may be missing.
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. RNAsubopt −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. Wuchty, W. Fontana, I. L. Hofacker and P. Schuster (1999), "Complete Suboptimal Folding of RNA and the Stability of Secondary Structures", Biopolymers: 49, pp 145-165
M. Zuker (1989), "On Finding All Suboptimal Foldings of an RNA Molecule", Science 244.4900, pp 48-52
Y. Ding, and C.E. Lawrence (2003), "A statistical sampling algorithm for RNA secondary structure prediction", Nucleic Acids Research 31.24, pp 7280-7301
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
Ivo L Hofacker, Stefan Wuchty, Walter Fontana, Ronny Lorenz
If in doubt our program is right, nature is at fault. Comments should be sent to firstname.lastname@example.org.