def mirmapredict(seq_mirna, seq_target):
seq_target_1 = "".join(map(lambda x: RULE2[x], seq_target.upper()))
mim = mirmap.mm(seq_target_1, seq_mirna)
mim.find_potential_targets_with_seed(allowed_lengths=[6, 7],
allowed_gu_wobbles={
6: 0,
7: 0
},
allowed_mismatches={
6: 0,
7: 0
},
take_best=True)
if len(mim.end_sites) != 0:
mim.eval_tgs_au(with_correction=False)
mim.eval_tgs_pairing3p(with_correction=False)
mim.eval_tgs_position(with_correction=False)
mim.eval_tgs_score(with_correction=False)
#mim.eval_score()
mim.libs = mirmap.library_link.LibraryLink(
'/home/f*x/tools/miRmap-1.1/libs/lib-archlinux-x86_64')
mim.exe_path = '/home/f*x/tools/miRmap-1.1/libs/exe-archlinux-x86_64'
mim.dg_duplex
mim.dg_open
mim.dg_binding
mim.prob_exact
mim.eval_score
outPut(mim.report())
def main ():
global options, args
# ****************************** main body *********************************
print 'Script for testing the miRmap library'
# Open files
target = open(options.target, 'r')
mirna = open(options.mirna, 'r')
# Show and save files contents:
print 'Target file:'
seq_target = target.read()
print seq_target
print 'miRNA sequence:'
seq_mirna = mirna.read()
print seq_mirna
# Perform different actions:
mim = mirmap.mm(seq_target, seq_mirna)
mim.find_potential_targets_with_seed(allowed_lengths=[6,7], allowed_gu_wobbles={6:0,7:0}, allowed_mismatches={6:0,7:0}, take_best=True)
print 'mim.end_sites: {}' . format(mim.end_sites) # Coordinate(s) (3' end) of the target site on the target sequence
mim.eval_tgs_au(with_correction=False) # TargetScan features manually evaluated with
mim.eval_tgs_pairing3p(with_correction=False) # a non-default parameter.
mim.eval_tgs_position(with_correction=False)
print 'mim.prob_binomial: {}' . format(mim.prob_binomial) # mim's attribute: the feature is automatically computed
print 'mim.report: {}' . format(mim.report())
def predict_on_mim(args):
mirna, transcript = args
mimset = mirmap.mm(transcript[1], mirna[1])
if shared.libs:
mimset.libs = shared.libs
if shared.exe_path:
mimset.exe_path = shared.exe_path
mimset.find_potential_targets_with_seed()
if len(mimset.end_sites) > 0:
shared.logger.debug('Evaluating mirna:%s transcript:%s'%(mirna[0], transcript[0]))
# De novo features
mimset.eval_tgs_au()
mimset.eval_tgs_position()
mimset.eval_tgs_pairing3p()
mimset.eval_tgs_score()
mimset.eval_dg_duplex()
mimset.eval_dg_open()
mimset.eval_dg_total()
mimset.eval_prob_exact()
mimset.eval_prob_binomial()
# Rest of the features
mimset.cons_blss = [0.] * len(mimset.end_sites)
mimset.selec_phylops = [1.] * len(mimset.end_sites)
if hasattr(shared, 'aln_path'):
aln_fname = os.path.join(shared.aln_path, '%s.fa'%(transcript[0]))
if os.path.exists(aln_fname):
if shared.mod_path:
mod_fname = os.path.join(shared.mod_path, '%s.mod'%(transcript[0]))
if os.path.exists(mod_fname):
with open(mod_fname) as modf:
mod = modf.read()
start = mod.find('TREE: ') + 6
end = mod.find(';', start) + 1
tree = mod[start:end]
mimset.eval_cons_bls(aln_fname=aln_fname, tree=tree, fitting_tree=False)
mimset.eval_selec_phylop(aln_fname=aln_fname, mod_fname=mod_fname)
else:
mimset.eval_cons_bls(aln_fname=aln_fname, tree='species.tree', fitting_tree=True)
mimset.eval_selec_phylop(aln_fname=aln_fname, mod_fname=mod_fname)
mimset.eval_score()
if shared.combine:
return mirna[0], transcript[0], mimset.end_sites, mimset.seed_lengths, mimset.nb_mismatches_except_gu_wobbles, mimset.nb_gu_wobbles, mimset.tgs_au, mimset.tgs_position, mimset.tgs_pairing3p, mimset.tgs_score, mimset.dg_duplex, mimset.dg_binding, mimset.dg_duplex_seed, mimset.dg_binding_seed, mimset.dg_open, mimset.dg_total, mimset.prob_exact, mimset.prob_binomial, mimset.cons_bls, mimset.selec_phylop, mimset.score
else:
return mirna[0], transcript[0], mimset.end_sites, mimset.seed_lengths, mimset.nb_mismatches_except_gu_wobbles, mimset.nb_gu_wobbles, mimset.tgs_aus, mimset.tgs_positions, mimset.tgs_pairing3ps, mimset.tgs_scores, mimset.dg_duplexs, mimset.dg_bindings, mimset.dg_duplex_seeds, mimset.dg_binding_seeds, mimset.dg_opens, mimset.dg_totals, mimset.prob_exacts, mimset.prob_binomials, mimset.cons_blss, mimset.selec_phylops, mimset.scores
def main():
global options, args
# ****************************** main body *********************************
print 'Script for testing the miRmap library'
# Open files
target = open(options.target, 'r')
mirna = open(options.mirna, 'r')
# Show and save files contents:
print 'Target file:'
seq_target = target.read()
print seq_target
print 'miRNA sequence:'
seq_mirna = mirna.read()
print seq_mirna
# Perform different actions:
mim = mirmap.mm(seq_target, seq_mirna)
mim.find_potential_targets_with_seed(allowed_lengths=[6, 7],
allowed_gu_wobbles={
6: 0,
7: 0
},
allowed_mismatches={
6: 0,
7: 0
},
take_best=True)
print 'mim.end_sites: {}'.format(
mim.end_sites
) # Coordinate(s) (3' end) of the target site on the target sequence
mim.eval_tgs_au(
with_correction=False) # TargetScan features manually evaluated with
mim.eval_tgs_pairing3p(with_correction=False) # a non-default parameter.
mim.eval_tgs_position(with_correction=False)
print 'mim.prob_binomial: {}'.format(
mim.prob_binomial
) # mim's attribute: the feature is automatically computed
print 'mim.report: {}'.format(mim.report())
