Python interface to LinearPartition, a linear-time RNA secondary structure prediction tool
Project description
python-linearpartition
Unofficial CPython binding to LinearPartition
Installation
Use pip
to install the module.
pip install linearpartition-unofficial
You may build from the source code for unsupported Python versions or platforms.
git clone --recursive https://github.com/ChangLabSNU/python-linearpartition
cd python-linearpartition
pip install .
Usage
The module currently only has one function called partition(seq)
.
The seq parameter should be an RNA sequence in uppercase letters,
and any T
should be converted to U
before passing it to the function.
>>> import linearpartition as lp
>>> seq = 'UGUCGGGGUUGGCUGUCUGACA'
>>> pred = lp.partition(seq)
>>> pred['free_energy']
-7.216465644007023
>>> pred['structure']
'(((((((........)))))))'
>>> import pandas as pd
>>> pd.DataFrame(pred['bpp']).sort_values('prob', ascending=False).head()
i j prob
19 3 18 0.999201
18 2 19 0.998801
17 1 20 0.997717
21 5 16 0.996692
22 4 17 0.996508
Functions
linearpartition.partition()
The linearpartition.partition
function is a Python C extension function that
calls LinearPartition to
perform a linear partitioning operation and get the base pairing probability
matrix.
linearpartition.partition(seq, mode='eterna', beamsize=100, dangles=2)
Parameters
seq
(required): A string containing the RNA sequence to be analyzed. The sequence must be in uppercase and only contain A, C, G, and U. This parameter is required.mode
(optional): The name of free energy parameters to use. Use'vienna'
for Vienna RNA parameters, or'eterna'
for EternaFold parameters.beamsize
(optional): An integer representing the beam size for the operation. Larger value requires more computational time and memory. The default value is 100.dangles
(optional): An integer representing the number of dangles for the partitioning operation. The default value is 2.
Return Value
This function returns a dictionary containing the MEA structure, base-pairing probability matrix and free energy of the ensemble structure in kcal/mol from the result of the partitioning operation.
Author
Hyeshik Chang <hyeshik@snu.ac.kr>
License
This Python binding is licensed under the MIT-style license. However, the compiled binary includes code from the LinearPartition package, which is licensed for non-commercial use.
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