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https://github.com/marcom/linearfold.jl
Julia interface to LinearFold RNA structure prediction programs: LinearFold, LinearPartition, LinearSampling, and LinearTurboFold
https://github.com/marcom/linearfold.jl
julia rna rna-folding rna-structure-prediction
Last synced: 3 months ago
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Julia interface to LinearFold RNA structure prediction programs: LinearFold, LinearPartition, LinearSampling, and LinearTurboFold
- Host: GitHub
- URL: https://github.com/marcom/linearfold.jl
- Owner: marcom
- License: other
- Created: 2022-05-29T20:53:45.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-20T19:25:36.000Z (10 months ago)
- Last Synced: 2024-09-21T22:41:39.440Z (4 months ago)
- Topics: julia, rna, rna-folding, rna-structure-prediction
- Language: Julia
- Homepage:
- Size: 36.1 KB
- Stars: 6
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Julia interface to LinearFold
[](https://github.com/marcom/LinearFold.jl/actions/workflows/CI.yml?query=branch%3Amain)
[](https://github.com/JuliaTesting/Aqua.jl)
Unofficial Julia interface to the
[LinearFold](https://github.com/LinearFold) suite of programs for RNA
secondary structure prediction. Please cite the applicable original
LinearFold, LinearPartition, LinearSampling, or LinearTurboFold
publications if you use this library.
The LinearFold, LinearPartition, LinearSampling, and LinearTurboFold
programs are supported at the moment. This library calls the binary
executables of these programs directly and parses their output.
The name LinearFold derives from the `O(n)` running time (where `n` is
the sequence length) of calculating an approximate solution, compared
to the typical cubic `O(n^3)` running time for the exact solution.
This speedup is achieved by recasting the normal dynamic programming
algorithms to work on the sequence left-to-right and by using a beam
search approximation. Most algorithms also have a linear or quadratic
time dependence on the beam size used. Please refer to the LinearFold
publications for further details.
## Installation
```
] add LinearFold
```
## Usage
```julia
using LinearFold, Unitful
```
### Keyword argument description
- `model=:vienna`: energy model to be used. Valid options are
`:vienna` and `:contrafold`. Default is `:vienna`.
- `beamsize=100`: size used for beam search approximation. Larger
numbers trade longer computation time for more precise
answers. Default is `100`.
- `constraints`: structural constraints of the predicted structure. A
string consisting of the characters '?', '.', '(', ')',
corresponding to positions that have unspecified base pairing,
unpaired, or base-pairing specified by matching parentheses.
- `is_sharpturn=false`: enable sharp turns in predictions. Default is
`false`.
- `verbose=false`: output extra information from the program runs to
stdout. Default is `false`.
### Minimum free energy (MFE) structure of an RNA strand
Uses the LinearFold program to predict the MFE and MFE structure.
```julia
# mfe(seq; model, beamsize, constraints, is_sharpturn, verbose)
mfe("GGGAAACCC") # => (-1.2 kcal mol^-1, "(((...)))")
mfe("GGGAAACCC"; constraints="?(.????)?") # => (0.9 kcal mol^-1, "((.....))")
mfe("GGGAAACCC"; model=:contrafold) # => (-0.09 kcal mol^-1, ".........")
```
### Base pair probabilities
Uses the LinearPartition program to calculate base pair probabilities.
```julia
# bpp(seq; model, beamsize, bpp_cutoff, is_sharpturn, verbose)
bpp("GGGAAACCC") # => (-1.62 kcal mol^-1, sparse(...))
bpp("GGGAAACCC"; bpp_cutoff=0.1)
```
### Pseudoknot structure prediction
Uses the LinearPartition program to predict possibly pseudoknotted
secondary structures with a beam search approximation to the
ThreshKnot algorithm.
Because the predicted structures can contain pseudoknots, the
structure is returned as a list of integers which indicate the
base-pairing partner of the current index.
```julia
# threshknot(seq; model, beamsize, threshold, is_sharpturn, verbose)
threshknot("GGGAAACCC") # => (-1.62 kcal mol^-1, [9, 8, 7, 0, 0, 0, 3, 2, 1])
threshknot("GGGAAACCC"; threshold=0.2)
```
### Sample structures from Boltzmann ensemble
Uses the LinearSampling program to return `num_samples` secondary
structures sampled according to their Boltzmann probability for an RNA
sequence.
```julia
# sample_structures(seq; beamsize, num_samples, is_nonsaving, is_sharpturn, verbose)
sample_structures("GGGAAACCC") # => [ "((....)).", ... ]
sample_structures("GGGAAACCC"; num_samples=100)
```
### Simultaneous alignment and folding
Uses the LinearTurboFold program to simultaneously align and fold
multiple RNA sequences.
```julia
# turbofold(sequences; beamsize_hmm, beamsize_cky, iterations,
# threshknot_min_helix_len,
# threshknot_iterations, threshknot_threshold,
# verbose)
turbofold(["GGGAAACCC", "GGCCAAAUGGCCA"])
```
### Maximum expected accuracy (MEA) structure
Uses the LinearPartition program.
```julia
# mea(seq; model, beamsize, gamma, is_sharpturn, verbose)
mea("GGGAAACCC") # => (-1.62 kcal mol^-1, "(((...)))")
mea("GGGAAACCC"; gamma=0.5) # => (-1.62 kcal mol^-1, ".(.....).")
```
### Zuker suboptimal structures
Uses the LinearFold program.
```julia
# zuker_subopt(seq; model, beamsize, delta, is_sharpturn, verbose)
zuker_subopt("GCGCGAAAAAACCCCCCC") # => [ (2.9 kcal mol^-1, "....(........)...."), ... ]
zuker_subopt("GCGCGAAAAAACCCCCCC"; delta=4.0u"kcal/mol")
```
### Energy of a (sequence, structure) pair
Uses the eval mode of the LinearFold program.
```julia
# energy(seq, structure; model, is_sharpturn, verbose)
energy("GGGAAACCC", "(((...)))") # => -1.2 kcal mol^-1
```
### Partition function only, no base pair probabilities
Uses the LinearPartition program.
```julia
# partfn(seq; model, beamsize, is_sharpturn, verbose)
partfn("GGGAAACCC") # => -1.62 kcal mol^-1
```
## Related Julia packages
- [ViennaRNA.jl](https://github.com/marcom/ViennaRNA.jl)
- [RNAstructure.jl](https://github.com/marcom/RNAstructure.jl)
- [PlotRNA.jl](https://github.com/marcom/PlotRNA.jl)