Exemplo n.º 1
0
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())
Exemplo n.º 2
0
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())
Exemplo n.º 3
0
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
Exemplo n.º 4
0
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())