RNAeval − manual page for RNAeval 2.4.11
RNAeval [OPTIONS] [<input0>] [<input1>]...
Determine the free energy of a (consensus) secondary structure for (an alignment of) RNA sequence(s)
free energy of a particular (consensus) secondary structure
for an (an alignment of) RNA molecule(s). The energy unit is
kcal/mol and contains a covariance pseudo−energy term
for multiple sequence alignments (−−msa
option) and corresponding consensus structures. The program
will continue to read new sequences and structures until a
line consisting of the single character "@" or an
end of file condition is encountered. If the input sequence
or structure contains the separator character
"&" the program calculates the energy of the
co−folding of two RNA strands, where the
"&" marks the boundary between the two
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
Below are command line options which alter the general behavior of this program
Do not automatically substitude nucleotide "T" with "U"
Print out energy contribution of each loop in the structure.
Split batch input into jobs and start processing in parallel using multiple threads. A value of 0 indicates to use as many parallel threads as computation cores are available.
Default processing of input data is performed in a serial fashion, i.e. one sequence at a time. Using this switch, a user can instead start the computation for many sequences in the input in parallel. RNAeval will create as many parallel computation slots as specified and assigns input sequences of the input file(s) to the available slots. Note, that this increases memory consumption since input alignments have to be kept in memory until an empty compute slot is available and each running job requires its own dynamic programming matrices.
Do not try to keep output in order with input while parallel processing is in place.
When parallel input processing (−−jobs flag) is enabled, the order in which input is processed depends on the host machines job scheduler. Therefore, any output to stdout or files generated by this program will most likely not follow the order of the corresponding input data set. The default of RNAeval is to use a specialized data structure to still keep the results output in order with the input data. However, this comes with a trade−off in terms of memory consumption, since all output must be kept in memory for as long as no chunks of consecutive, ordered output are available. By setting this flag, RNAeval will not buffer individual results but print them as soon as they have been computated.
Read a file instead of reading from stdin
The default behavior of RNAeval is to read input from stdin or the file(s) that follow(s) the RNAeval command. Using this parameter the user can specify input file names where data is read from. Note, that any additional files supplied to RNAeval are still processed as well.
Input is multiple sequence alignment in Stockholm 1.0 format
Using this flag indicates that the input is a multiple sequence alignment (MSA) instead of (a) single sequence(s). Note, that only STOCKHOLM format allows one to specify a consensus structure. Therefore, this is the only supported MSA format for now!
Automatically generate an ID for each sequence. (default=off)
The default mode of RNAeval 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, RNAeval 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. 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 alignment 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.
Rescale energy parameters to a temperature of temp C. Default is 37C.
Do not include special tabulated stabilizing energies for tri−, tetra− and hexaloop hairpins. Mostly for testing.
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. 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.
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.
Read energy parameters from paramfile, instead of using the default parameter set.
A sample parameter file should accompany your distribution. See the RNAlib documentation for details on the file format.
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.
Assume a circular (instead of linear) RNA molecule.
Incoorporate G−Quadruplex formation into the structure prediction algorithm
Recalculate energies of structures using a logarithmic energy function for multi−loops before output.
This option does not effect structure generation, only the energies that are printed out. Since logML lowers energies somewhat, some structures may be missing.
Use SHAPE reactivity data in the folding recursions (does not work for PF yet)
−−shapeMethod=[D/Z/W] + [optional parameters]
Specify the method how to convert SHAPE
reactivity data to pseudo energy
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.
+ [optional parameters] Specify the method used to convert SHAPE
reactivities to pairing probabilities when
using the SHAPE approach of 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)
Output "most informative sequence" instead of simple consensus: For each column of the alignment output the set of nucleotides with frequency greater than average in IUPAC notation.
Set the weight of the covariance term in the energy function
Set the penalty for non−compatible sequences in the covariance term of the energy function
use specified Ribosum Matrix instead of normal
energy model. Matrixes to use should be 6x6
matrices, the order of the terms is AU, CG, GC, GU, UA, UG.
use ribosum scoring matrix. The matrix is chosen according to the minimal and maximal pairwise identities of the sequences in the file.
use old energy evaluation, treating gaps as characters.
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
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 email@example.com.