def run_anc(input_dict,run_id = None):
assert run_id
rank_name = input_dict['rank_name']
taxon_id = input_dict['taxid']
aliname = input_dict['aliname']
BT = getBTOL()
p_node = ncbi.get_node(taxon_id)
seqnodes = BT.investigatePhylum(p_node = p_node)
recs, seqelts, seqtuples = seq_recs(seqnodes)
align = align_seqnodes(recs)
tree = phyml.tree(align, run_id = run_id)
rstfile= paml.run_paml(tree, align, run_id = run_id)
anc_tree = paml.rst_parser(rstfile)
anc_alignment = [SeqRecord(elt.m['seq'],
id = None,
name = elt.name,
annotations = {'scores':elt.m['probs']})
for elt in anc_tree.get_nonterminals()]
out_dict = dict(anc_tree=anc_tree,
anc_align= anc_alignment,
term_tree = tree,
term_align = align)
return out_dict
def run(**kwargs):
BT = getBTOL(**mem.sr(kwargs))
seqnodes = BT.investigatePhylum(**kwargs)
recs, seqelts, seqtuples = seq_recs(seqnodes)
align = align_seqnodes(recs)
tree = phyml.tree(align)
rstfile= paml.run_paml(tree, align)
anc_tree = paml.rst_parser(rstfile)
anc_alignment = [SeqRecord(elt.m['seq'],
id = None,
name = elt.name,
annotations = {'scores':elt.m['probs']})
for elt in anc_tree.get_nonterminals()]
return (tree, anc_tree), (align, anc_alignment)
def get_ml_ancestor_tree(tree, ali, run_id):
print 'Running ancestor inference in PAML'
#RUN PAML
rstfile= paml.run_paml(tree, ali, run_id = run_id)
anc_tree = paml.rst_parser(rstfile)
#Label extent and internal nodes with sequences.
for term in anc_tree.get_terminals():
#COPY OLD TERMINAL NODES TO THE NEW TREE
term.m = filter( lambda x: x.name == term.name,
tree.get_terminals())[0].m
for node in anc_tree.get_nonterminals():
#REPLACE Ts WITH US IN THE TERMINALS OF THE NEW TREE
node.m['seq'].seq = node.m['seq'].seq.replace('T', 'U')
return anc_tree
def get_consensus(rfid = 'RF00', mweight = .5,
refseq_method = 'root', sp_method = 'sample',
aff_type = 'pairs', reset = True,
do_plot = False, run_id = 'CONS_TEST'):
ali, tree, infos = rfam.get_fam(rfid)
ali_ids = [a.name for a in ali]
for i, n in enumerate(tree.get_terminals()):
term_id = re.compile('_([^_]*)_').search(n.name).group(1)
this_seq = ali[ali_ids.index(term_id)]
n.m = {'seq':this_seq,
'probs':[1 for j in range(len(this_seq))]}
#if do_plot : rplots.plot_clusters(inds,{'pca embedding':pca_vecs},title = title,plot3d = True)
big_refnode, big_refseq = \
subtree_refseq(tree, method = refseq_method)
ungapped_ref = rutils.ungapped_seq(big_refseq, rfid)
#pca_vecs,exemplar_structs =
return family_exemplar_structs(rfid,
sp_method = sp_method,
refseq_method = refseq_method,
aff_type = aff_type,
)
struct_profiles = infernal.profiles(ungapped_ref,exemplar_structs, run_id)
clades = split_tree(tree)
all_vecs = {'all_time':[ [ [] for i in range(len(struct_profiles))]
for j in range(len(clades)) ],
'all_mut':[ [ [] for i in range(len(struct_profiles))]
for j in range(len(clades)) ],
'fiftyfifty':[ [ [] for i in range(len(struct_profiles))]
for j in range(len(clades)) ]}
aamuts, aatimes, aairr, aagaps = [], [], [], []
for idx_clade, c in enumerate(clades):
if len(c.get_terminals()) < 3:
print 'SKIPPPING CUZ SUBTREE TOO SMALL'
continue
c_ids = [ n.m['seq'].name for n in c.get_terminals() ]
if len(nonzero(greater([len(list(g)) for k, g in it.groupby(sorted(c_ids))],1))[0])>0:
print 'SKIPPING CUZ THERE ARE TWO COPIES OF SOME F*****G SEQUENCE IN TREE'
continue
all_muts, all_times , all_gaps, all_irr = [], [], [], []
print
print 'Clade: {0}'.format(idx_clade)
for idx_struct, struct_info in enumerate( zip( struct_profiles, exemplar_structs)):
struct_profile, ex_struct = struct_info
ngaps = 0
#OLD ALIGNMENTS
calis = ba.MultipleSeqAlignment(\
[n.m['seq'] for n in c.get_terminals() ])
#NEW ALIGNMENTS AND REF STRUCTURE
c_new_ali , stk, struct = infernal.alignment(calis, struct_profile, rfid)
#REF STRUCTURE PAIRS
pairs = rutils.stk_pairs(struct)
if len(pairs) != len(ex_struct):
raise Exception()
cterms = c.get_terminals()
for i2, ct in enumerate(cterms):
lilid = 'N{0}'.format(i2)
ct.name = lilid
ct.m['str_seq'] = c_new_ali[i2]
ct.m['str_seq'].id = lilid
ct.m['probs'] = ones(len(c_new_ali[i2]))
#BUILD A TREE
tr = phy.BaseTree.Tree(c)
#RUN PAML
paml_run_id = 'ali_anc_c{0:04}_s{0:03}'.format(idx_clade,idx_struct)
rstfile= paml.run_paml(tr, c_new_ali, run_id = paml_run_id)
anc_tree = paml.rst_parser(rstfile)
#Label extent and internal nodes with sequences.
for term in anc_tree.get_terminals():
#Terminals have old (rfam) alis and new (infernal) alis
term.m = filter( lambda x: x.name == term.name, cterms)[0].m
for node in anc_tree.get_nonterminals():
#Internals only have new alis. m['seq'] = m['str_seq']
node.m['str_seq'] = node.m['seq']
node.m['str_seq'].seq = node.m['str_seq'].seq.replace('T', 'U')
subtree = anc_tree
#Evaluate all of the structs on the first pass
#to have access to mean frequencies of different
#mutational types in the final score computation
refnode, refseq = subtree_refseq(subtree, method = refseq_method)
muts, times, gaps, irresolvables = subtree_count_struct(subtree, pairs)
all_muts.append(muts)
all_times.append(times)
all_gaps.append(gaps)
all_irr.append(irresolvables)
compute_signatures(all_vecs,idx_clade,
all_muts,all_times,
exemplar_structs,ungapped_ref )
aamuts.append(all_muts)
aatimes.append(all_times)
aairr.append(all_irr)
aagaps.append(all_gaps)
outputs = {
'all_vecs':all_vecs,
'all_muts':aamuts,
'all_times':aatimes,
'exemplar_structs':exemplar_structs,
'reference_seq':ungapped_ref,
'thermo_ex_inds':inds,
'thermo_embedding':pca_vecs,
'title':title,
'thermo_aff_type':aff_type,
'tree':tree,
'run_id':run_id
}
pickle.dump(outputs, open(cfg.dataPath('cs874/runs/{0}.pickle'.format(run_id)),'w'))
return(outputs)