Python Examples

MFE Prediction (flat interface)

import RNA

# The RNA sequence
seq = "GAGUAGUGGAACCAGGCUAUGUUUGUGACUCGCAGACUAACA"

# compute minimum free energy (MFE) and corresponding structure
(ss, mfe) = RNA.fold(seq)

# print output
print "%s\n%s [ %6.2f ]" % (seq, ss, mfe)

MFE Prediction (object oriented interface)

import RNA;

sequence = "CGCAGGGAUACCCGCG"

# create new fold_compound object
fc = RNA.fold_compound(sequence)

# compute minimum free energy (mfe) and corresponding structure
(ss, mfe) = fc.mfe()

# print output
print "%s [ %6.2f ]" % (ss, mfe)

Suboptimal Structure Prediction

import RNA

sequence = "GGGGAAAACCCC"

# Set global switch for unique ML decomposition
RNA.cvar.uniq_ML = 1

subopt_data = { 'counter' : 1, 'sequence' : sequence }

# Print a subopt result as FASTA record
def print_subopt_result(structure, energy, data):
    if not structure == None:
        print ">subopt %d" % data['counter']
        print "%s" % data['sequence']
        print "%s [%6.2f]" % (structure, energy)
        # increase structure counter
        data['counter'] = data['counter'] + 1

# Create a 'fold_compound' for our sequence
a = RNA.fold_compound(sequence)

# Enumerate all structures 500 dacal/mol = 5 kcal/mol arround
# the MFE and print each structure using the function above
a.subopt_cb(500, print_subopt_result, subopt_data);

Boltzmann Sampling (a.k.a. Probabilistic Backtracing)

import RNA

sequence = "UGGGAAUAGUCUCUUCCGAGUCUCGCGGGCGACGGGCGAUCUUCGAAAGUGGAAUCCGUACUUAUACCGCCUGUGCGGACUACUAUCCUGACCACAUAGU"

"""
A simple callback function that stores
a structure sample into a list
"""
def store_structure(s, data):
    if s:
        data.append(s)


"""
First we prepare a fold_compound object
"""

# create model details
md = RNA.md()

# activate unique multibranch loop decomposition
md.uniq_ML = 1

# create fold compound object
fc = RNA.fold_compound(sequence, md)

# compute MFE
(ss, mfe) = fc.mfe()

# rescale Boltzmann factors according to MFE
fc.exp_params_rescale(mfe)

# compute partition function to fill DP matrices
fc.pf()

"""
Now we are ready to perform Boltzmann sampling
"""

# 1. backtrace a single sub-structure of length 10
print "%s" % fc.pbacktrack5(10)


# 2. backtrace a single sub-structure of length 50
print "%s" % fc.pbacktrack5(50)

# 3. backtrace multiple sub-structures of length 10 at once
for s in fc.pbacktrack5(20, 10):
    print "%s" % s

# 4. backtrace multiple sub-structures of length 50 at once
for s in fc.pbacktrack5(100, 50):
    print "%s" % s

# 5. backtrace a single structure (full length)
print "%s" % fc.pbacktrack()

# 6. backtrace multiple structures at once
for s in fc.pbacktrack(100):
    print "%s" % s

# 7. backtrace multiple structures non-redundantly
for s in fc.pbacktrack(100, RNA.PBACKTRACK_NON_REDUNDANT):
    print "%s" % s

# 8. backtrace multiple structures non-redundantly (with resume option)
num_samples = 500
iterations  = 15
d           = None # pbacktrack memory object
s_list      = list()

for i in range(0, iterations):
    d, ss   = fc.pbacktrack(num_samples, d, RNA.PBACKTRACK_NON_REDUNDANT)
    s_list  = s_list + list(ss)

for s in s_list:
    print "%s" % s

# 9. backtrace multiple sub-structures of length 50 in callback mode
ss  = list()
i   = fc.pbacktrack5(100, 50, store_structure, ss)

for s in ss:
    print "%s" % s

# 10. backtrace multiple full-length structures in callback mode
ss  = list()
i   = fc.pbacktrack(100, store_structure, ss)

for s in ss:
    print "%s" % s

# 11. non-redundantly backtrace multiple full-length structures in callback mode
ss  = list()
i   = fc.pbacktrack(100, store_structure, ss, RNA.PBACKTRACK_NON_REDUNDANT)

for s in ss:
    print "%s" % s

# 12. non-redundantly backtrace multiple full length structures
# in callback mode with resume option
ss = list()
d  = None # pbacktrack memory object

for i in range(0, iterations):
    d, i = fc.pbacktrack(num_samples, store_structure, ss, d, RNA.PBACKTRACK_NON_REDUNDANT)

for s in ss:
    print "%s" % s

RNAfold -p –MEA equivalent

#!/usr/bin/python
#

import RNA

seq = "AUUUCCACUAGAGAAGGUCUAGAGUGUUUGUCGUUUGUCAGAAGUCCCUAUUCCAGGUACGAACACGGUGGAUAUGUUCGACGACAGGAUCGGCGCACUA"

# create fold_compound data structure (required for all subsequently applied  algorithms)
fc = RNA.fold_compound(seq)

# compute MFE and MFE structure
(mfe_struct, mfe) = fc.mfe()

# rescale Boltzmann factors for partition function computation
fc.exp_params_rescale(mfe)

# compute partition function
(pp, pf) = fc.pf()

# compute centroid structure
(centroid_struct, dist) = fc.centroid()

# compute free energy of centroid structure
centroid_en = fc.eval_structure(centroid_struct)

# compute MEA structure
(MEA_struct, MEA) = fc.MEA()

# compute free energy of MEA structure
MEA_en = fc.eval_structure(MEA_struct)

# print everything like RNAfold -p --MEA
print("%s\n%s (%6.2f)" % (seq, mfe_struct, mfe))
print("%s [%6.2f]" % (pp, pf))
print("%s {%6.2f d=%.2f}" % (centroid_struct, centroid_en, dist))
print("%s {%6.2f MEA=%.2f}" % (MEA_struct, MEA_en, MEA))
print(" frequency of mfe structure in ensemble %g; ensemble diversity %-6.2f" % (fc.pr_structure(mfe_struct), fc.mean_bp_distance()))

Fun with Soft Constraints

import RNA

seq1 = "CUCGUCGCCUUAAUCCAGUGCGGGCGCUAGACAUCUAGUUAUCGCCGCAA"

# Turn-off dangles globally
RNA.cvar.dangles = 0

# Data structure that will be passed to our MaximumMatching() callback with two components:
# 1. a 'dummy' fold_compound to evaluate loop energies w/o constraints, 2. a fresh set of energy parameters
mm_data = { 'dummy': RNA.fold_compound(seq1), 'params': RNA.param() }

# Nearest Neighbor Parameter reversal functions
revert_NN = { 
    RNA.DECOMP_PAIR_HP:       lambda i, j, k, l, f, p: - f.eval_hp_loop(i, j) - 100,
    RNA.DECOMP_PAIR_IL:       lambda i, j, k, l, f, p: - f.eval_int_loop(i, j, k, l) - 100,
    RNA.DECOMP_PAIR_ML:       lambda i, j, k, l, f, p: - p.MLclosing - p.MLintern[0] - (j - i - k + l - 2) * p.MLbase - 100,
    RNA.DECOMP_ML_ML_STEM:    lambda i, j, k, l, f, p: - p.MLintern[0] - (l - k - 1) * p.MLbase,
    RNA.DECOMP_ML_STEM:       lambda i, j, k, l, f, p: - p.MLintern[0] - (j - i - k + l) * p.MLbase,
    RNA.DECOMP_ML_ML:         lambda i, j, k, l, f, p: - (j - i - k + l) * p.MLbase,
    RNA.DECOMP_ML_ML_ML:      lambda i, j, k, l, f, p: 0,
    RNA.DECOMP_ML_UP:         lambda i, j, k, l, f, p: - (j - i + 1) * p.MLbase,
    RNA.DECOMP_EXT_STEM:      lambda i, j, k, l, f, p: - f.E_ext_loop(k, l),
    RNA.DECOMP_EXT_EXT:       lambda i, j, k, l, f, p: 0,
    RNA.DECOMP_EXT_STEM_EXT:  lambda i, j, k, l, f, p: - f.E_ext_loop(i, k),
    RNA.DECOMP_EXT_EXT_STEM:  lambda i, j, k, l, f, p: - f.E_ext_loop(l, j),
    RNA.DECOMP_EXT_EXT_STEM1: lambda i, j, k, l, f, p: - f.E_ext_loop(l, j-1),
            }

# Maximum Matching callback function (will be called by RNAlib in each decomposition step)
def MaximumMatching(i, j, k, l, d, data):
    return revert_NN[d](i, j, k, l, data['dummy'], data['params'])

# Create a 'fold_compound' for our sequence
fc = RNA.fold_compound(seq1)

# Add maximum matching soft-constraints
fc.sc_add_f(MaximumMatching)
fc.sc_add_data(mm_data, None)

# Call MFE algorithm
(s, mm) = fc.mfe()

# print result
print "%s\n%s (MM: %d)\n" %  (seq1, s, -mm)