def main(sto_filename, output_prefix, ntaxa, cmdf, shuffle_iter):
"""
Takes <sto_filename>, randomly picks <ntaxa> species,
"""
msa = MSA(sto_filename)
n = random.sample(range(msa.nseq), ntaxa)
msa.nseq = ntaxa
msa.ids = ['T'+str(i) for i in xrange(ntaxa)]
msa.aln = [msa.aln[i] for i in n]
msa.trim_gaps(removeAmbs=True, threshold=1.) # remove just ambs and all-gap cols
file1 = output_prefix + '.original.sto'
msa.write_stockholm(file1)
cmdf.write("python $GBPML/run_pfold.py " + file1 + '\n')
cmdf.write("python $GBPML/run_gbpml.py -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml.py_log -o {0}.gbpml.tree\n".format(file1))
cmdf.write("python $GBPML/run_gbpml.py -n -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml_nobp.py_log -o {0}.gbpml_nobp.tree\n".format(file1))
cmdf.write("bash $GBPML/run_dnaml-erate.sh " + file1 + '\n')
cmdf.write("find {0}*.tree|xargs -n1 -i bash $GBPML/scripts/run_pscore.sh {0} {{}}\n".format(file1))
if shuffle_iter > 0:
for iter in xrange(shuffle_iter):
file2 = output_prefix + '.shuffle_iter' + str(iter) + '.sto'
msa.shuffle_cols()
msa.write_stockholm(file2)
cmdf.write("python $GBPML/run_pfold.py " + file2 + '\n')
cmdf.write("python $GBPML/run_gbpml.py -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml.py_log -o {0}.gbpml.tree\n".format(file2))
cmdf.write("python $GBPML/run_gbpml.py -n -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml_nobp.py_log -o {0}.gbpml_nobp.tree\n".format(file2))
cmdf.write("bash $GBPML/run_dnaml-erate.sh " + file2 + '\n')
cmdf.write("find {0}*.tree|xargs -n1 -i bash $GBPML/scripts/run_pscore.sh {0} {{}}\n".format(file2))
return True
def main(sto_filename, output_prefix, trim_gap_threshold, singlify_threshold, ntaxa, d, cmdf):
"""
Preparing for the rRNA concordance test.
Takes <sto_filename>, randomly picks <ntaxa> species,
a) removes all paired cols w/ too little canonical pairs (<singlify_threshold>) or ambiguous code
b) removes all unpaired cols w/ too much gaps (<trim_gap_threshold>) or ambiguous code
Filles up dict <d> with: <prefix base> --> ids, stats of pre/post filtering
"""
msa = MSA(sto_filename)
n = random.sample(range(msa.nseq), ntaxa)
msa.nseq = ntaxa
msa.ids = [msa.ids[i] for i in n]
msa.aln = [msa.aln[i] for i in n]
d_key = os.path.basename(output_prefix)
d[d_key] = {}
d[d_key]['ids'] = msa.ids
d[d_key]['pre'] = msa.get_stats()
msa.write_stockholm(output_prefix+'.raw.sto')
print >> sys.stderr, "delete pair cols ambiguous or has canonical less than ", singlify_threshold
msa.singlify_pairs(singlify_threshold, delete_instead_of_singlify=True, removeAmb=True)
print >> sys.stderr, "delete single cols ambiguous or more gaps % than", trim_gap_threshold
msa.trim_gaps(removeAmbs=True, threshold=trim_gap_threshold)
msa.write_stockholm(output_prefix+'.original.sto')
old = msa.get_stats()['single']
d[d_key]['post'] = msa.get_stats()
msa.trim_gaps(removeAmbs=True, threshold=trim_gap_threshold) # sanity check
assert msa.get_stats()['single'] == old #just sanity check
file1, file2 = halve_msa(msa, output_prefix)
cmdf.write("python $GBPML/run_pfold.py " + file1 + '\n')
cmdf.write("python $GBPML/run_gbpml.py -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml.py_log -o {0}.gbpml.tree\n".format(file1))
cmdf.write("python $GBPML/run_gbpml.py -n -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml_nobp.py_log -o {0}.gbpml_nobp.tree\n".format(file1))
cmdf.write("bash $GBPML/run_dnaml-erate.sh " + file1 + '\n')
cmdf.write("python $GBPML/run_pfold.py " + file2 + '\n')
cmdf.write("python $GBPML/run_gbpml.py -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml.py_log -o {0}.gbpml.tree\n".format(file2))
cmdf.write("python $GBPML/run_gbpml.py -n -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml_nobp.py_log -o {0}.gbpml_nobp.tree\n".format(file2))
cmdf.write("bash $GBPML/run_dnaml-erate.sh " + file2 + '\n')
# o_sto = output_prefix+'.original.sto'
# print("python $GBPML/run_pfold.py " + o_sto)
# print("python $GBPML/run_gbpml.py -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml.py_log -o {0}.gbpml.tree".format(o_sto))
# print("python $GBPML/run_gbpml.py -n -t {0}.fasta.mltree.tree -a {0} -f {0}.gbpml_nobp.py_log -o {0}.gbpml_nobp.tree".format(o_sto))
# print("bash $GBPML/run_dnaml-erate.sh " + o_sto)
return True
return args
if __name__ == "__main__":
options = usage()
msa_filename = options.msa_filename
tree_filename = options.tree_filename
single_model_filename = options.single_model
paired_model_filename = options.paired_model
treat_gap_as_missing = options.treat_gap_as_missing
assert 0. < options.trim_gap_threshold <= 1.
assert 1 <= options.cpu
msa = MSA(msa_filename, options.ignore_bp)
msa.trim_gaps(removeAmbs=True, threshold=options.trim_gap_threshold)
single_model = SingleModel(single_model_filename)
paired_model = PairedModel(paired_model_filename, single_model)
# -------------- using dendropy -------------------
t = dendropy.Tree.get_from_path(tree_filename, 'newick')
# have to call remove_seqs_not_in_tree becuz sometimes I
# will manually trim leaves from the tree
msa.remove_seqs_not_in_tree([x.taxon.label for x in t.leaf_nodes()])
# edge lengths of 0 will cause calculation problems...
# TODO: better way to handle this?
for n in t.nodes():
if n.edge_length <= 0:
n.edge_length = 1e-3
print >> sys.stderr, "Node {0} has an edge length of 0. Manually padded to 0.001. Remove this node in the future to avoid this".format(n)
nnode_p, ncol_p, nbase_p = P.shape
P = scipy.ascontiguousarray(P.reshape(P.size))
like_s, like_s_n_p, S, P = calc_likelihood(msa, order, single_model, paired_model) # need to use this to set up S, P for rearr
return like_s_n_p
if __name__ == "__main__":
from MSA import *
from EvoModel import *
from Tree import *
usage()
msa = MSA(msa_filename)
msa.trim_gaps(0.7)
single_model = SingleModel(single_model_filename)
paired_model = PairedModel(paired_model_filename, single_model)
# --------------- using newick ---------------------
# acc = list(msa.ids)
# post_order_traversal(t, acc)
# order = acc[msa.nseq:]
# -------------- using dendropy -------------------
t2 = dendropy.Tree.get_from_path(tree_filename, 'newick')
msa.remove_seqs_not_in_tree([x.taxon.label for x in t2.leaf_nodes()])
t = t2
order = postorder_assign_then_traverse(t, list(msa.ids))
raw_input("break")
