RNALfold - manual page for RNALfold 2.6.4


RNALfold [OPTION]...


RNALfold 2.6.4

calculate locally stable secondary structures of RNAs

Compute locally stable RNA secondary structure with a maximal base pair span. For a sequence of length n and a base pair span 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 RNA structures. Output consists of a list of secondary structure components of size <= L, one entry per line. Each output line contains the predicted local structure its energy in kcal/mol and the starting position of the local structure.

-h, --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

-v, --verbose

Be verbose.


I/O Options:

Command line options for input and output (pre-)processing

-i, --infile=filename

Read a file instead of reading from stdin

The default behavior of RNALfold is to read input from stdin. Using this parameter the user can specify an input file name where data is read from.

-o, --outfile[=filename]

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 number of output files created for batch input (multiple sequences) depends on three conditions: (i) In case an optional filename is given as parameter argument, a single file with the specified filename will be written into. If the optional argument is omitted, (ii) FASTA input or an active --auto-id switch will write to multiple files that follow the naming scheme “prefix.lfold”. Here, “prefix” is taken from the sequence id as specified in the FASTA header. Lastly, (iii) single-line sequence input without FASTA header will be written to a single file “RNALfold_output.lfold”. In case an output file already exists, any output of the program will be appended to it. Since the filename argument is optional, it must immediately follow the short option flag to not be mistaken as new parameter to the program. For instance ``-ornafold.out`` will write to a file “rnafold.out”. 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)


Do not automatically substitute nucleotide “T” with “U”.



Automatically generate an ID for each sequence. (default=off)

The default mode of RNALfold 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, RNALfold 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).


If this parameter is set, each sequence will be prefixed with the provided string. Hence, the output files will obey the following naming scheme: “prefix_xxxx.lfold” 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).


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.


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 IDs.


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 used in sanitized filenames.


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_ss.ps” for secondary structure plots. With this flag set, no truncation of the output filenames is done, 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).


Select additional algorithms which should be included in the calculations. The Minimum free energy (MFE) and a structure representative are calculated in any case.

-L, --span=INT

Set the maximum distance between any two pairing nucleotides.


This option specifies the window length L and therefore the upper limit for the distance between the bases i and j of any pair (i, j), i.e. (j - i + 1) <= L.

-z, --zscore[=DOUBLE]

Limit the output to predictions with a Z-score below a threshold.


This option activates z-score regression using a trained SVM. Any predicted structure that exceeds the specified threshold will be ommited from the output. Since the Z-score threshold is given as a negative number, it must immediately preceed the short option to not be mistaken as a separate argument, e.g. -z-2.9 sets the threshold to a value of -2.9


Apply the z-score filtering in the forward recursions.


The default mode of z-score filtering considers the entire structure space to decide whether or not a locally optimal structure at any position i is reported or not. When using this post-filtering step, however, alternative locally optimal structures

starting at i with higher energy but lower z-score can be easily missed. The


option restricts the structure space already in the forward recursions, such


only optimal solution among those candidates that satisfy the z-score

threshold are considered. Therefore, good results according to the z-score threshold criterion are less likely to be superseded by results with better energy but worse z-score. Note, that activating this switch results in higher computation time which scales linear in the window length.


Report subsumed structures if their z-score is less than that of the enclosing structure.


In default mode, RNALfold only reports locally optimal structures if they are no constituents of another, larger structure with less free energy. In z-score mode, however, such a larger structure may have a higher z-score, thus may be less informative than the smaller substructure. Using this switch activates reporting both, the smaller and the larger structure if the z-score of the smaller is lower than that of the larger.

-b, --backtrack-global

Backtrack a global MFE structure. (default=off)

Instead of just reporting the locally stable secondary structure a global MFE structure can be constructed that only consists of locally optimal substructures. This switch activates a post-processing step that takes the locally optimal structures to generate the global MFE structure which constitutes the MFE value reported in the last line. The respective global MFE structure is printed just after the inut sequence part on the last line, preceding the global MFE score. Note, that this option implies -o/–outfile since the locally optimal structures must be read after the regular prediction step! Also note, that using this option in combination with -z/–zscore implies --zscore-pre-filter to ensure proper construction of the global MFE structure!

-g, --gquad

Incoorporate G-Quadruplex formation into the structure prediction algorithm.


Structure Constraints:

Command line options to interact with the structure constraints feature of this program


Use SHAPE reactivity data to guide structure predictions.


Select SHAPE reactivity data incorporation strategy.


The following methods can be used to convert SHAPE reactivities into pseudo energy contributions.

D: Convert by using the linear equation according to Deigan et al 2009.

Derived pseudo energy terms 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.


Select method for SHAPE reactivity conversion.


This parameter is useful when dealing with the SHAPE incorporation according to Zarringhalam et al. 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.

Energy Parameters:

Energy parameter sets can be adapted or loaded from user-provided input files

-T, --temp=DOUBLE

Rescale energy parameters to a temperature of temp C. Default is 37C.


-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. The placeholder file name DNA can be used to load DNA parameters without the need to actually specify any input file.

-4, --noTetra

Do not include special tabulated stabilizing energies for tri-, tetra- and hexaloop hairpins.


Mostly for testing.


Set salt concentration in molar (M). Default is 1.021M.

-m, --modifications[=STRING]

Allow for modified bases within the RNA sequence string.


Treat modified bases within the RNA sequence differently, i.e. use corresponding energy corrections and/or pairing partner rules if available. For that, the modified bases in the input sequence must be marked by their corresponding one-letter code. If no additional arguments are supplied, all available corrections are performed. Otherwise, the user may limit the modifications to a particular subset of modifications, resp. one-letter codes, e.g. -mP6 will only correct for pseudouridine and m6A bases.

Currently supported one-letter codes and energy corrections are:

7: 7-deaza-adenonsine (7DA)

I: Inosine

6: N6-methyladenosine (m6A)

P: Pseudouridine

9: Purine (a.k.a. nebularine)

D: Dihydrouridine


Use additional modified base data from JSON file.

Model Details:

Tweak the energy model and pairing rules additionally using the following parameters

-d, --dangles=INT

How to treat “dangling end” energies for bases adjacent to helices in free ends and multi-loops.


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).


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.



Do not allow GU pairs at the end of helices.



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. --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 helical rise of the helix in units of Angstrom.


Use with caution! This value will be re-set automatically to 3.4 in case DNA parameters are loaded via -P DNA and no further value is provided.


Set the average backbone length for looped regions in units of Angstrom.


Use with caution! This value will be re-set automatically to 6.76 in case DNA parameters are loaded via -P DNA and no further value is provided.


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

I.L. Hofacker, B. Priwitzer, and P.F. Stadler (2004), “Prediction of Locally Stable RNA Secondary Structures for Genome-Wide Surveys”, Bioinformatics: 20, pp 186-190

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, Peter F Stadler, Ronny Lorenz


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


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