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https://github.com/marcom/viennarna_cbinding.jl

Low-level Julia interface to ViennaRNA via CBinding
https://github.com/marcom/viennarna_cbinding.jl

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Low-level Julia interface to ViennaRNA via CBinding

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# ViennaRNA_CBinding.jl

Low-level Julia interface to ViennaRNA via CBinding



Unofficial Julia interface to the

[ViennaRNA](https://github.com/ViennaRNA/ViennaRNA) library for RNA

secondary structure prediction and analysis.  Please cite the original

ViennaRNA publications if you use this library.



These are low-level bindings to the ViennaRNA library made with CBinding, and

require C standard header files to be installed at runtime to work.



If you don't need to be able to access struct fields not exposed by the ViennaRNA API,

you might find the

[ViennaRNA.jl](https://github.com/marcom/ViennaRNA.jl)

Julia package more convenient.



## Usage



```julia

using ViennaRNA_CBinding

const ViennaRNA = ViennaRNA_CBinding



# Notes

# The original C API functions can be found in the submodule `ViennaRNA.LibRNA`.

# Most functions can be called with a String containing the RNA sequence,

# e.g. `ViennaRNA.mfe("GGGAAACCC")`.



# sequence to be folded

# uniq_ML=1 (unique multiloop decomposition) is needed for some functions,

# e.g. pbacktrack

fc = FoldCompound("GGGGGAAAAACCCCCC"; uniq_ML=1)



# minimum free energy structure (MFE) for a sequence

# please excuse the excess precision printed when displaying -9.4 kcal/mol

ViennaRNA.mfe(fc)                         # => ("(((((.....))))).", -9.399999618530273 kcal mol^-1)



# partition function

ViennaRNA.partfn(fc)                      # => ("(((((.....})))),", -9.81672191619873 kcal mol^-1)



# calculate energy for structure

ViennaRNA.energy(fc, "((((.......)))).")  # => -6.199999809265137 kcal mol^-1



# basepair probabilities

ViennaRNA.bpp(fc)                         # => 16×16 Matrix{Float64}



# Boltzmann probability of a structure

ViennaRNA.prob_of_structure(fc, "(((((.....))))).")  # => 0.5085737925408758



# ensemble defect

ViennaRNA.ensemble_defect(fc, "(((((.....))))).")  # => 0.3035942605397949



# probabilistic / stochastic backtrack, sample from Boltzmann ensemble of

# secondary structures

ViennaRNA.pbacktrack(fc)                  # => [ "((((......)))).." ]

ViennaRNA.pbacktrack(fc; num_samples=10)  # => 10-element Vector{String}



# all suboptimal structures with energies delta above the MFE

# structure

using Unitful    # to be able to write 4u"kcal/mol" with @u_str

ViennaRNA.subopt(fc; delta=4u"kcal/mol")  # => Vector{Tuple{String, Quantity}}



# basepair distance between secondary structures

ViennaRNA.bp_distance("....", "(())")     # => 2



# mean basepair distance of all structures to each other,

# weighted by the structure's Boltzmann probabilities

ViennaRNA.mean_bp_distance(fc)            # => 5.266430215905888



# centroid structure of ensemble: structure with smallest sum of

# base-pair distances weighted by Boltzmann probabilities

ViennaRNA.centroid(fc)                    # => ("(((((.....))))).", 4.799131457924728)



# Maximum expected accuracy (MEA) structure. The gamma parameter

# trades off specificity (low gamma) and sensitivity (high gamma).

ViennaRNA.mea(fc; gamma=1.0)              # => ("(((((.....))))).", 10.706348f0)



# plot coordinates of a secondary structure, returns two arrays with

# x and y coordinates

ViennaRNA.plot_coords("(((...)))")        # => Tuple{Float32[], Float32[]}



# inverse folding / sequence design

ViennaRNA.inverse_fold("AAAAAAA", "((...))")    # => ("GCAAAGC", 2.0f0)

ViennaRNA.inverse_pf_fold("AAAAAAA", "((...))") # => ("GCCAAGC", 2.0244526863098145 kcal mol^-1)

```