def run_epa(self):
self.cfg.log.info(
"Running RAxML-EPA to place %d query sequences...\n" %
self.query_count)
raxml = RaxmlWrapper(config)
reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
self.refjson.get_raxml_readable_tree(reftree_fname)
optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.refjson.get_binary_model(optmod_fname)
job_name = self.cfg.subst_name("epa_%NAME%")
reftree_str = self.refjson.get_raxml_readable_tree()
reftree = Tree(reftree_str)
self.reftree_size = len(reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
reduced_align_fname = raxml.reduce_alignment(self.epa_alignment)
jp = raxml.run_epa(job_name, reduced_align_fname, reftree_fname,
optmod_fname)
raxml.copy_epa_jplace(job_name, self.out_jplace_fname, move=True)
return jp
def get_nodesheight(self):
root = Tree(self.tree)
nh_map = {}
for node in root.traverse(strategy = "preorder"):
if hasattr(node, "B"):
height = node.get_closest_leaf(topology_only=True)
#height = node.get_farthest_leaf(topology_only=True)
nh_map[node.B] = height[1] + 1
return nh_map
def load_refjson(self, refjson_fname):
try:
self.refjson = RefJsonParser(refjson_fname)
except ValueError:
self.cfg.exit_user_error("ERROR: Invalid json file format!")
#validate input json format
(valid, err) = self.refjson.validate()
if not valid:
self.cfg.log.error(
"ERROR: Parsing reference JSON file failed:\n%s", err)
self.cfg.exit_user_error()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.bid_taxonomy_map = self.refjson.get_branch_tax_map()
if not self.bid_taxonomy_map:
# old file format (before 1.6), need to rebuild this map from scratch
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(self.tax_tree)
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(self.bid_taxonomy_map, final=False)
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg,
self.bid_taxonomy_map,
self.rate, self.node_height)
self.taxtree_helper = TaxTreeHelper(self.cfg, self.origin_taxonomy,
self.tax_tree)
tax_code_name = self.refjson.get_taxcode()
self.tax_code = TaxCode(tax_code_name)
self.taxonomy = Taxonomy(prefix=EpacConfig.REF_SEQ_PREFIX,
tax_map=self.origin_taxonomy)
self.tax_common_ranks = self.taxonomy.get_common_ranks()
# print "Common ranks: ", self.tax_common_ranks
self.mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
self.rank_mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
def test_taxtree_builder(self):
cfg = EpacConfig()
testfile_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testfiles")
tax_fname = os.path.join(testfile_dir, "test.tax")
tax = Taxonomy(EpacConfig.REF_SEQ_PREFIX, tax_fname)
tree_fname = os.path.join(testfile_dir, "taxtree.nw")
expected_tree = Tree(tree_fname, format=8)
tb = TaxTreeBuilder(cfg, tax)
tax_tree, seq_ids = tb.build()
self.assertEqual(seq_ids, tax.get_map().keys())
self.assertEqual(tax_tree.write(format=8), expected_tree.write(format=8))
def setUp(self):
self.testfile_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "testfiles")
self.tax_fname = os.path.join(self.testfile_dir, "test_clean.tax")
self.taxonomy = Taxonomy(EpacConfig.REF_SEQ_PREFIX, self.tax_fname)
tax_map = self.taxonomy.get_map()
cfg = EpacConfig()
self.taxtree_helper = TaxTreeHelper(cfg, tax_map)
outgr_fname = os.path.join(self.testfile_dir, "outgroup.nw")
self.expected_outgr = Tree(outgr_fname)
def test_taxtree_builder(self):
cfg = EpacConfig()
testfile_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testfiles")
tax_fname = os.path.join(testfile_dir, "test.tax")
tax = Taxonomy(EpacConfig.REF_SEQ_PREFIX, tax_fname)
tree_fname = os.path.join(testfile_dir, "taxtree.nw")
expected_tree = Tree(tree_fname, format=8)
tb = TaxTreeBuilder(cfg, tax)
tax_tree, seq_ids = tb.build()
self.assertEqual(seq_ids, tax.get_map().keys())
self.assertEqual(tax_tree.write(format=8), expected_tree.write(format=8))
def test_outgroup(self):
mfu_tree_fname = os.path.join(self.testfile_dir, "taxtree.nw")
mfu_tree = Tree(mfu_tree_fname)
self.taxtree_helper.set_mf_rooted_tree(mfu_tree)
outgr = self.taxtree_helper.get_outgroup()
self.assertEqual(outgr.get_leaf_names(),
self.expected_outgr.get_leaf_names())
def setUp(self):
self.testfile_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testfiles")
self.tax_fname = os.path.join(self.testfile_dir, "test_clean.tax")
self.taxonomy = Taxonomy(EpacConfig.REF_SEQ_PREFIX, self.tax_fname)
tax_map = self.taxonomy.get_map()
cfg = EpacConfig()
self.taxtree_helper = TaxTreeHelper(cfg, tax_map)
outgr_fname = os.path.join(self.testfile_dir, "outgroup.nw")
self.expected_outgr = Tree(outgr_fname)
def epa_2_ptp(epa_jp, ref_jp, full_alignment, min_lw = 0.5, debug = False):
placements = epa_jp.get_placement()
reftree = Tree(epa_jp.get_std_newick_tree())
allnodes = reftree.get_descendants()
species_list = []
placemap = {}
"""find how many edges are used for placement, and create a map to store """
for placement in placements:
edges = placement["p"]
curredge = edges[0][0]
lw = edges[0][2]
if lw >= min_lw:
placemap[curredge] = placemap.get(curredge, [])
"""group taxa name by placement branch"""
for placement in placements:
edges = placement["p"]
taxa_names = placement["n"]
curredge = edges[0][0]
lw = edges[0][2]
if lw >= min_lw:
a = placemap[curredge]
a.extend(taxa_names)
placemap[curredge] = a
groups = placemap.items()
cnt_leaf = 0
cnt_inode = 0
"""check each placement edge"""
for i,item in enumerate(groups):
place_branch_name = item[0]
seqset = item[1]
if len(seqset) < 4:
species_list.append(seqset)
else:
branch_alignment = SeqGroup()
for taxa in seqset:
branch_alignment.set_seq(taxa, full_alignment.get_seq(taxa))
species = build_tree_run_ptp(branch_alignment, ref_jp.get_rate())
species_list.extend(species)
return species_list
def get_speciation_rate(self):
#pruning the input tree such that each species only appear once
species = set()
keepseqs = []
for name in self.taxonomy.keys():
ranks = self.taxonomy[name]
sp = ranks[-1]
if sp == "-":
keepseqs.append(name)
else:
if not sp in species:
keepseqs.append(name)
species.add(sp)
root = Tree(self.tree)
root.prune(keepseqs, preserve_branch_length=True)
sumbr = 0.0
cnt = 0.0
for node in root.traverse(strategy = "preorder"):
sumbr = sumbr + node.dist
cnt = cnt + 1.0
return float(cnt) / float(sumbr)
def load_refjson(self, refjson_fname):
try:
self.refjson = RefJsonParser(refjson_fname)
except ValueError:
self.cfg.exit_user_error("ERROR: Invalid json file format!")
#validate input json format
(valid, err) = self.refjson.validate()
if not valid:
self.cfg.log.error("ERROR: Parsing reference JSON file failed:\n%s", err)
self.cfg.exit_user_error()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.bid_taxonomy_map = self.refjson.get_branch_tax_map()
if not self.bid_taxonomy_map:
# old file format (before 1.6), need to rebuild this map from scratch
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(self.tax_tree)
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(self.bid_taxonomy_map, final=False)
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.rate, self.node_height)
self.taxtree_helper = TaxTreeHelper(self.cfg, self.origin_taxonomy, self.tax_tree)
tax_code_name = self.refjson.get_taxcode()
self.tax_code = TaxCode(tax_code_name)
self.taxonomy = Taxonomy(prefix=EpacConfig.REF_SEQ_PREFIX, tax_map=self.origin_taxonomy)
self.tax_common_ranks = self.taxonomy.get_common_ranks()
# print "Common ranks: ", self.tax_common_ranks
self.mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
self.rank_mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
def epa_post_process(self):
lbl_tree = Tree(self.reftree_lbl_str)
self.taxtree_helper.set_bf_unrooted_tree(lbl_tree)
self.reftree_tax = self.taxtree_helper.get_tax_tree()
self.bid_ranks_map = self.taxtree_helper.get_bid_taxonomy_map()
if self.cfg.debug:
self.reftree_tax.write(outfile=self.reftree_tax_fname, format=3)
with open(self.reftree_lbl_fname, "w") as outf:
outf.write(self.reftree_lbl_str)
with open(self.brmap_fname, "w") as outf:
for bid, br_rec in self.bid_ranks_map.iteritems():
outf.write("%s\t%s\t%d\t%f\n" %
(bid, br_rec[0], br_rec[1], br_rec[2]))
def get_speciation_rate_fast(self):
"""ETE2 prune() function is extremely slow on large trees, so
this function don't use it and instead just removes "redundant"
species-level nodes one-by-one"""
species = set()
root = Tree(self.tree)
name2node = {}
for node in root.traverse(strategy = "postorder"):
if node.is_leaf():
name2node[node.name] = node
#pruning the input tree such that each species only appear once
for name in self.taxonomy.keys():
ranks = self.taxonomy[name]
sp = ranks[-1]
if sp != "-":
if sp in species:
node = name2node.get(name, None)
if node:
node.delete(preserve_branch_length=True)
else:
raise ValueError("Node names not found in the tree: " + name)
else:
species.add(sp)
# traverse the pruned tree, counting the number of speciation events and
# summing up the branch lengths
sumbr = 0.0
cnt = 0
for node in root.traverse(strategy = "preorder"):
sumbr += node.dist
cnt += 1
# sp_rate = number_of_sp_events / sum_of_branch_lengts
return float(cnt) / float(sumbr)
def __init__(self, config, args):
self.cfg = config
self.method = args.method
self.minlw = args.min_lhw
self.jplace_fname = args.jplace_fname
self.ranktest = args.ranktest
self.output_fname = args.output_dir + "/" + args.output_name
# switch off branch length filter
self.brlen_pv = 0.
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
self.reftree_lbl_fname = config.tmp_fname("%NAME%_lbl.tre")
self.reftree_tax_fname = config.tmp_fname("%NAME%_tax.tre")
self.optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
try:
self.refjson = RefJsonParser(config.refjson_fname, ver="1.2")
except ValueError:
print("Invalid json file format!")
sys.exit()
#validate input json format
self.refjson.validate()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.bid_taxonomy_map = self.refjson.get_bid_tanomomy_map()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.brlen_pv, self.rate, self.node_height)
self.TAXONOMY_RANKS_COUNT = 10
self.mislabels = []
self.mislabels_cnt = [0] * self.TAXONOMY_RANKS_COUNT
self.rank_mislabels = []
self.rank_mislabels_cnt = [0] * self.TAXONOMY_RANKS_COUNT
self.misrank_conf_map = {}
def test_jplace_read(self):
jplace_fname = os.path.join(self.testfile_dir, "test.jplace")
parser = EpaJsonParser(jplace_fname)
self.assertEqual(parser.get_raxml_version(), "8.2.3")
t = Tree(parser.get_tree())
t_len = len(t)
self.assertEqual(t_len, 32)
self.assertEqual(len(parser.get_placement()), 6)
for p in parser.get_placement():
self.assertFalse(p["n"][0] in t)
self.assertTrue(len(p["p"]) > 0)
for edge in p["p"]:
branch = int(edge[0])
lh = edge[1]
lhw = edge[2]
self.assertTrue(branch >= 0 and branch < (t_len * 2 - 3))
self.assertTrue(lhw >= 0.0 and lhw <= 1.0)
class TaxTreeHelperTests(unittest.TestCase):
def setUp(self):
self.testfile_dir = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "testfiles")
self.tax_fname = os.path.join(self.testfile_dir, "test_clean.tax")
self.taxonomy = Taxonomy(EpacConfig.REF_SEQ_PREFIX, self.tax_fname)
tax_map = self.taxonomy.get_map()
cfg = EpacConfig()
self.taxtree_helper = TaxTreeHelper(cfg, tax_map)
outgr_fname = os.path.join(self.testfile_dir, "outgroup.nw")
self.expected_outgr = Tree(outgr_fname)
def tearDown(self):
self.taxonomy = None
self.taxtree_helper = None
def test_outgroup(self):
mfu_tree_fname = os.path.join(self.testfile_dir, "taxtree.nw")
mfu_tree = Tree(mfu_tree_fname)
self.taxtree_helper.set_mf_rooted_tree(mfu_tree)
outgr = self.taxtree_helper.get_outgroup()
self.assertEqual(outgr.get_leaf_names(),
self.expected_outgr.get_leaf_names())
def test_branch_labeling(self):
bfu_tree_fname = os.path.join(self.testfile_dir, "resolved_tree.nw")
bfu_tree = Tree(bfu_tree_fname)
map_fname = os.path.join(self.testfile_dir, "bid_tax_map.txt")
self.expected_map = {}
with open(map_fname) as inf:
for line in inf:
bid, rank_id, rdiff, brlen = line.strip().split("\t")
self.expected_map[bid] = (rank_id, int(rdiff), float(brlen))
self.taxtree_helper.set_outgroup(self.expected_outgr)
self.taxtree_helper.set_bf_unrooted_tree(bfu_tree)
bid_tax_map = self.taxtree_helper.get_bid_taxonomy_map()
self.assertEqual(len(bid_tax_map), 2 * len(bfu_tree) - 3)
for bid in self.expected_map.iterkeys():
e_rec = self.expected_map[bid]
rec = bid_tax_map[bid]
self.assertEqual(e_rec[0], rec[0])
self.assertEqual(e_rec[1], rec[1])
self.assertAlmostEqual(e_rec[2], rec[2], 6)
class TaxTreeHelperTests(unittest.TestCase):
def setUp(self):
self.testfile_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testfiles")
self.tax_fname = os.path.join(self.testfile_dir, "test_clean.tax")
self.taxonomy = Taxonomy(EpacConfig.REF_SEQ_PREFIX, self.tax_fname)
tax_map = self.taxonomy.get_map()
cfg = EpacConfig()
self.taxtree_helper = TaxTreeHelper(cfg, tax_map)
outgr_fname = os.path.join(self.testfile_dir, "outgroup.nw")
self.expected_outgr = Tree(outgr_fname)
def tearDown(self):
self.taxonomy = None
self.taxtree_helper = None
def test_outgroup(self):
mfu_tree_fname = os.path.join(self.testfile_dir, "taxtree.nw")
mfu_tree = Tree(mfu_tree_fname)
self.taxtree_helper.set_mf_rooted_tree(mfu_tree)
outgr = self.taxtree_helper.get_outgroup()
self.assertEqual(outgr.get_leaf_names(), self.expected_outgr.get_leaf_names())
def test_branch_labeling(self):
bfu_tree_fname = os.path.join(self.testfile_dir, "resolved_tree.nw")
bfu_tree = Tree(bfu_tree_fname)
map_fname = os.path.join(self.testfile_dir, "bid_tax_map.txt")
self.expected_map = {}
with open(map_fname) as inf:
for line in inf:
bid, rank_id, rdiff, brlen = line.strip().split("\t")
self.expected_map[bid] = (rank_id, int(rdiff), float(brlen))
self.taxtree_helper.set_outgroup(self.expected_outgr)
self.taxtree_helper.set_bf_unrooted_tree(bfu_tree)
bid_tax_map = self.taxtree_helper.get_bid_taxonomy_map()
self.assertEqual(len(bid_tax_map), 2 * len(bfu_tree) - 3)
for bid in self.expected_map.iterkeys():
e_rec = self.expected_map[bid]
rec = bid_tax_map[bid]
self.assertEqual(e_rec[0], rec[0])
self.assertEqual(e_rec[1], rec[1])
self.assertAlmostEqual(e_rec[2], rec[2], 6)
def test_branch_labeling(self):
bfu_tree_fname = os.path.join(self.testfile_dir, "resolved_tree.nw")
bfu_tree = Tree(bfu_tree_fname)
map_fname = os.path.join(self.testfile_dir, "bid_tax_map.txt")
self.expected_map = {}
with open(map_fname) as inf:
for line in inf:
bid, rank_id, rdiff, brlen = line.strip().split("\t")
self.expected_map[bid] = (rank_id, int(rdiff), float(brlen))
self.taxtree_helper.set_outgroup(self.expected_outgr)
self.taxtree_helper.set_bf_unrooted_tree(bfu_tree)
bid_tax_map = self.taxtree_helper.get_bid_taxonomy_map()
self.assertEqual(len(bid_tax_map), 2 * len(bfu_tree) - 3)
for bid in self.expected_map.iterkeys():
e_rec = self.expected_map[bid]
rec = bid_tax_map[bid]
self.assertEqual(e_rec[0], rec[0])
self.assertEqual(e_rec[1], rec[1])
self.assertAlmostEqual(e_rec[2], rec[2], 6)
def classify(self, query_fname, fout = None, method = "1", minlw = 0.0, pv = 0.02, minp = 0.9, ptp = False):
if self.jplace_fname:
jp = EpaJsonParser(self.jplace_fname)
else:
self.checkinput(query_fname, minp)
raxml = RaxmlWrapper(config)
reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
self.refjson.get_raxml_readable_tree(reftree_fname)
optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.refjson.get_binary_model(optmod_fname)
job_name = self.cfg.subst_name("epa_%NAME%")
reftree_str = self.refjson.get_raxml_readable_tree()
reftree = Tree(reftree_str)
self.reftree_size = len(reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
reduced_align_fname = raxml.reduce_alignment(self.epa_alignment)
jp = raxml.run_epa(job_name, reduced_align_fname, reftree_fname, optmod_fname)
placements = jp.get_placement()
if fout:
fo = open(fout, "w")
else:
fo = None
output2 = ""
for place in placements:
output = None
taxon_name = place["n"][0]
origin_taxon_name = EpacConfig.strip_query_prefix(taxon_name)
edges = place["p"]
# edges = self.erlang_filter(edges, p = pv)
if len(edges) > 0:
ranks, lws = self.classify_helper.classify_seq(edges, method, minlw)
isnovo = self.novelty_check(place_edge = str(edges[0][0]), ranks =ranks, lws = lws, minlw = minlw)
rankout = self.print_ranks(ranks, lws, minlw)
if rankout == None:
output2 = output2 + origin_taxon_name+ "\t\t\t?\n"
else:
output = "%s\t%s\t" % (origin_taxon_name, self.print_ranks(ranks, lws, minlw))
if isnovo:
output += "*"
else:
output +="o"
if self.cfg.verbose:
print(output)
if fo:
fo.write(output + "\n")
else:
output2 = output2 + origin_taxon_name+ "\t\t\t?\n"
if os.path.exists(self.noalign):
with open(self.noalign) as fnoa:
lines = fnoa.readlines()
for line in lines:
taxon_name = line.strip()[1:]
origin_taxon_name = EpacConfig.strip_query_prefix(taxon_name)
output = "%s\t\t\t?" % origin_taxon_name
if self.cfg.verbose:
print(output)
if fo:
fo.write(output + "\n")
if self.cfg.verbose:
print(output2)
if fo:
fo.write(output2)
fo.close()
#############################################
#
# EPA-PTP species delimitation
#
#############################################
if ptp:
full_aln = SeqGroup(self.epa_alignment)
species_list = epa_2_ptp(epa_jp = jp, ref_jp = self.refjson, full_alignment = full_aln, min_lw = 0.5, debug = self.cfg.debug)
if self.cfg.verbose:
print "Species clusters:"
if fout:
fo2 = open(fout+".species", "w")
else:
fo2 = None
for sp_cluster in species_list:
translated_taxa = []
for taxon in sp_cluster:
origin_taxon_name = EpacConfig.strip_query_prefix(taxon)
translated_taxa.append(origin_taxon_name)
s = ",".join(translated_taxa)
if fo2:
fo2.write(s + "\n")
if self.cfg.verbose:
print s
if fo2:
fo2.close()
#############################################
if not self.jplace_fname:
if not self.cfg.debug:
raxml.cleanup(job_name)
FileUtils.remove_if_exists(reduced_align_fname)
FileUtils.remove_if_exists(reftree_fname)
FileUtils.remove_if_exists(optmod_fname)
def run_final_epa_test(self):
self.reftree_outgroup = self.refjson.get_outgroup()
tmp_reftree = self.reftree.copy(method="newick")
name2refnode = {}
for leaf in tmp_reftree.iter_leaves():
name2refnode[leaf.name] = leaf
tmp_taxtree = self.tax_tree.copy(method="newick")
name2taxnode = {}
for leaf in tmp_taxtree.iter_leaves():
name2taxnode[leaf.name] = leaf
for mis_rec in self.mislabels:
rname = mis_rec['name']
# rname = EpacConfig.REF_SEQ_PREFIX + name
if rname in name2refnode:
name2refnode[rname].delete()
else:
print "Node not found in the reference tree: %s" % rname
if rname in name2taxnode:
name2taxnode[rname].delete()
else:
print "Node not found in the taxonomic tree: %s" % rname
# remove unifurcation at the root
if len(tmp_reftree.children) == 1:
tmp_reftree = tmp_reftree.children[0]
self.mislabels = []
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(tmp_taxtree)
epa_result = self.run_epa_once(tmp_reftree)
reftree_epalbl_str = epa_result.get_std_newick_tree()
placements = epa_result.get_placement()
# update branchid-taxonomy mapping to account for possible changes in branch numbering
reftree_tax = Tree(reftree_epalbl_str)
th.set_bf_unrooted_tree(reftree_tax)
bid_tax_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(bid_tax_map, final=True)
cl = TaxClassifyHelper(self.cfg, bid_tax_map, self.rate,
self.node_height)
# newtax_fname = self.cfg.subst_name("newtax_%NAME%.tre")
# th.get_tax_tree().write(outfile=newtax_fname, format=3)
final_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# EXPERIMENTAL FEATURE - disabled for now!
# It could happen that certain ranks were present in the "original" reference tree, but
# are completely missing in the pruned tree (e.g., all seqs of a species were considered "suspicious"
# after the leave-one-out test and thus pruned)
# In this case, EPA has no chance to infer full original taxonomic annotation (=species) since the corresponding clade
# is now missing. To account for this fact, we amend the original taxonomic annotation and set ranks missing from
# pruned tree to "Undefined".
# orig_ranks = th.strip_missing_ranks(orig_ranks)
# print orig_ranks
# get EPA tax label
ranks, lws = cl.classify_seq(place["p"])
final_ass[seq_name] = (ranks, lws)
#print seq_name, ": ", orig_ranks, "--->", ranks
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks,
lws)
self.write_assignments(final_ass, final=True)
def classify(self, query_fname, minp = 0.9, ptp = False):
if self.jplace_fname:
jp = EpaJsonParser(self.jplace_fname)
else:
self.checkinput(query_fname, minp)
self.cfg.log.info("Running RAxML-EPA to place %d query sequences...\n" % self.query_count)
raxml = RaxmlWrapper(config)
reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
self.refjson.get_raxml_readable_tree(reftree_fname)
optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.refjson.get_binary_model(optmod_fname)
job_name = self.cfg.subst_name("epa_%NAME%")
reftree_str = self.refjson.get_raxml_readable_tree()
reftree = Tree(reftree_str)
self.reftree_size = len(reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
reduced_align_fname = raxml.reduce_alignment(self.epa_alignment)
jp = raxml.run_epa(job_name, reduced_align_fname, reftree_fname, optmod_fname)
raxml.copy_epa_jplace(job_name, self.out_jplace_fname, move=True)
self.cfg.log.info("Assigning taxonomic labels based on EPA placements...\n")
placements = jp.get_placement()
if self.out_assign_fname:
fo = open(self.out_assign_fname, "w")
else:
fo = None
noassign_list = []
for place in placements:
taxon_name = place["n"][0]
origin_taxon_name = EpacConfig.strip_query_prefix(taxon_name)
edges = place["p"]
if len(edges) > 0:
ranks, lws = self.classify_helper.classify_seq(edges)
isnovo = self.novelty_check(place_edge = str(edges[0][0]), ranks=ranks, lws=lws)
rankout = self.print_ranks(ranks, lws, self.cfg.min_lhw)
if rankout == None:
noassign_list.append(origin_taxon_name)
else:
output = "%s\t%s\t" % (origin_taxon_name, rankout)
if isnovo:
output += "*"
else:
output +="o"
if self.cfg.verbose:
print(output)
if fo:
fo.write(output + "\n")
else:
noassign_list.append(origin_taxon_name)
if os.path.exists(self.noalign):
with open(self.noalign) as fnoa:
lines = fnoa.readlines()
for line in lines:
taxon_name = line.strip()[1:]
origin_taxon_name = EpacConfig.strip_query_prefix(taxon_name)
noassign_list.append(origin_taxon_name)
for taxon_name in noassign_list:
output = "%s\t\t\t?" % origin_taxon_name
if self.cfg.verbose:
print(output)
if fo:
fo.write(output + "\n")
if fo:
fo.close()
#############################################
#
# EPA-PTP species delimitation
#
#############################################
if ptp:
full_aln = SeqGroup(self.epa_alignment)
species_list = epa_2_ptp(epa_jp = jp, ref_jp = self.refjson, full_alignment = full_aln, min_lw = 0.5, debug = self.cfg.debug)
self.cfg.log.debug("Species clusters:")
if fout:
fo2 = open(fout+".species", "w")
else:
fo2 = None
for sp_cluster in species_list:
translated_taxa = []
for taxon in sp_cluster:
origin_taxon_name = EpacConfig.strip_query_prefix(taxon)
translated_taxa.append(origin_taxon_name)
s = ",".join(translated_taxa)
if fo2:
fo2.write(s + "\n")
self.cfg.log.debug(s)
if fo2:
fo2.close()
class LeaveOneTest:
def __init__(self, config):
self.cfg = config
self.mis_fname = self.cfg.out_fname("%NAME%.mis")
self.premis_fname = self.cfg.out_fname("%NAME%.premis")
self.misrank_fname = self.cfg.out_fname("%NAME%.misrank")
self.stats_fname = self.cfg.out_fname("%NAME%.stats")
if os.path.isfile(self.mis_fname):
print("\nERROR: Output file already exists: %s" % self.mis_fname)
print(
"Please specify a different job name using -n or remove old output files."
)
self.cfg.exit_user_error()
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
self.reftree_lbl_fname = config.tmp_fname("%NAME%_lbl.tre")
self.reftree_tax_fname = config.tmp_fname("%NAME%_tax.tre")
self.optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
self.mislabels = []
self.mislabels_cnt = []
self.rank_mislabels = []
self.rank_mislabels_cnt = []
self.misrank_conf_map = {}
def write_bid_tax_map(self, bid_tax_map, final):
if self.cfg.debug:
fname_suffix = "final" if final else "l1out"
bid_fname = self.cfg.tmp_fname("%NAME%_" +
"bid_tax_map_%s.txt" % fname_suffix)
with open(bid_fname, "w") as outf:
for bid, bid_rec in bid_tax_map.items():
outf.write("%s\t%s\t%d\t%f\n" %
(bid, bid_rec[0], bid_rec[1], bid_rec[2]))
def write_assignments(self, assign_map, final):
if self.cfg.debug:
fname_suffix = "final" if final else "l1out"
assign_fname = self.cfg.tmp_fname("%NAME%_" + "taxassign_%s.txt" %
fname_suffix)
with open(assign_fname, "w") as outf:
for seq_name in assign_map.keys():
ranks, lws = assign_map[seq_name]
outf.write("%s\t%s\t%s\n" %
(seq_name, ";".join(ranks), ";".join(
["%.3f" % l for l in lws])))
def load_refjson(self, refjson_fname):
try:
self.refjson = RefJsonParser(refjson_fname)
except ValueError:
self.cfg.exit_user_error("ERROR: Invalid json file format!")
#validate input json format
(valid, err) = self.refjson.validate()
if not valid:
self.cfg.log.error(
"ERROR: Parsing reference JSON file failed:\n%s", err)
self.cfg.exit_user_error()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.bid_taxonomy_map = self.refjson.get_branch_tax_map()
if not self.bid_taxonomy_map:
# old file format (before 1.6), need to rebuild this map from scratch
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(self.tax_tree)
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(self.bid_taxonomy_map, final=False)
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg,
self.bid_taxonomy_map,
self.rate, self.node_height)
self.taxtree_helper = TaxTreeHelper(self.cfg, self.origin_taxonomy,
self.tax_tree)
tax_code_name = self.refjson.get_taxcode()
self.tax_code = TaxCode(tax_code_name)
self.taxonomy = Taxonomy(prefix=EpacConfig.REF_SEQ_PREFIX,
tax_map=self.origin_taxonomy)
self.tax_common_ranks = self.taxonomy.get_common_ranks()
# print "Common ranks: ", self.tax_common_ranks
self.mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
self.rank_mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
def run_epa_trainer(self):
epa_trainer.run_trainer(self.cfg)
if not os.path.isfile(self.cfg.refjson_fname):
self.cfg.log.error(
"\nBuilding reference tree failed, see error messages above.")
self.cfg.exit_fatal_error()
def classify_seq(self, placement):
edges = placement["p"]
if len(edges) > 0:
return self.classify_helper.classify_seq(edges)
else:
print("ERROR: no placements! something is definitely wrong!")
def check_seq_tax_labels(self, seq_name, orig_ranks, ranks, lws):
mis_rec = None
num_common_ranks = len(self.tax_common_ranks)
orig_rank_level = Taxonomy.lowest_assigned_rank_level(orig_ranks)
new_rank_level = Taxonomy.lowest_assigned_rank_level(ranks)
#if new_rank_level < 0 or (new_rank_level < num_common_ranks and orig_rank_level >= num_common_ranks):
# if new_rank_level < 0:
if len(ranks) == 0:
mis_rec = {}
mis_rec['name'] = seq_name
mis_rec['orig_level'] = -1
mis_rec['real_level'] = 0
mis_rec['level_name'] = "[NotIngroup]"
mis_rec['inv_level'] = -1 * mis_rec[
'real_level'] # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = []
mis_rec['lws'] = [1.0]
mis_rec['conf'] = mis_rec['lws'][0]
else:
mislabel_lvl = -1
min_len = min(len(orig_ranks), len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[
rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.tax_code.guess_rank_level(
orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = seq_name
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.tax_code.rank_level_name(
real_lvl)[0]
mis_rec['inv_level'] = -1 * mis_rec[
'real_level'] # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
if mis_rec:
self.mislabels.append(mis_rec)
return mis_rec
def filter_mislabels(self):
filtered_mis = []
for i in range(len(self.mislabels)):
if self.mislabels[i]['conf'] >= self.cfg.conf_cutoff:
filtered_mis.append(self.mislabels[i])
self.mislabels = filtered_mis
def check_rank_tax_labels(self, rank_name, orig_ranks, ranks, lws):
mislabel_lvl = -1
min_len = min(len(orig_ranks), len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[
rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.tax_code.guess_rank_level(orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = rank_name
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.tax_code.rank_level_name(real_lvl)[0]
mis_rec['inv_level'] = -1 * real_lvl # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
self.rank_mislabels.append(mis_rec)
return mis_rec
else:
return None
def mis_rec_to_string_old(self, mis_rec):
lvl = mis_rec['orig_level']
output = mis_rec['name'] + "\t"
output += "%s\t%s\t%s\t%.3f\n" % (
mis_rec['level_name'], mis_rec['orig_ranks'][lvl],
mis_rec['ranks'][lvl], mis_rec['lws'][lvl])
output += ";".join(mis_rec['orig_ranks']) + "\n"
output += ";".join(mis_rec['ranks']) + "\n"
output += "\t".join(["%.3f" % conf for conf in mis_rec['lws']]) + "\n"
return output
def mis_rec_to_string(self, mis_rec):
lvl = mis_rec['orig_level']
uncorr_name = EpacConfig.strip_ref_prefix(
self.refjson.get_uncorr_seqid(mis_rec['name']))
uncorr_orig_ranks = self.refjson.get_uncorr_ranks(
mis_rec['orig_ranks'])
uncorr_ranks = self.refjson.get_uncorr_ranks(mis_rec['ranks'])
output = uncorr_name + "\t"
if lvl >= 0:
output += "%s\t%s\t%s\t%.3f\t" % (
mis_rec['level_name'], uncorr_orig_ranks[lvl],
uncorr_ranks[lvl], mis_rec['lws'][lvl])
else:
output += "%s\t%s\t%s\t%.3f\t" % (mis_rec['level_name'], "NA",
"NA", mis_rec['lws'][0])
output += Taxonomy.lineage_str(uncorr_orig_ranks) + "\t"
output += Taxonomy.lineage_str(uncorr_ranks) + "\t"
output += ";".join(["%.3f" % conf for conf in mis_rec['lws']])
if 'rank_conf' in mis_rec:
output += "\t%.3f" % mis_rec['rank_conf']
return output
def sort_mislabels(self):
self.mislabels = sorted(self.mislabels,
key=itemgetter('inv_level', 'conf', 'name'),
reverse=True)
for mis_rec in self.mislabels:
real_lvl = mis_rec["real_level"]
self.mislabels_cnt[real_lvl] += 1
if self.cfg.ranktest:
self.rank_mislabels = sorted(self.rank_mislabels,
key=itemgetter(
'inv_level', 'conf', 'name'),
reverse=True)
for mis_rec in self.rank_mislabels:
real_lvl = mis_rec["real_level"]
self.rank_mislabels_cnt[real_lvl] += 1
def write_stats(self, toFile=False):
self.cfg.log.info("Mislabeled sequences by rank:")
seq_sum = 0
rank_sum = 0
stats = []
for i in range(len(self.mislabels_cnt)):
if i > 0:
rname = self.tax_code.rank_level_name(i)[0].ljust(12)
else:
rname = "[NotIngroup]"
if self.mislabels_cnt[i] > 0:
seq_sum += self.mislabels_cnt[i]
# output = "%s:\t%d" % (rname, seq_sum)
output = "%s:\t%d" % (rname, self.mislabels_cnt[i])
if self.cfg.ranktest:
rank_sum += self.rank_mislabels_cnt[i]
output += "\t%d" % rank_sum
self.cfg.log.info(output)
stats.append(output)
if toFile:
with open(self.stats_fname, "w") as fo_stat:
for line in stats:
fo_stat.write(line + "\n")
def write_mislabels_header(self, fo, final, fields):
header = ";" + "\t".join(fields) + "\n"
# write to file
if final:
for line in DISCLAIMER.split("\n"):
fo.write(";%s\n" % line)
fo.write(";\n")
fo.write(header)
# print to console
if final and self.cfg.verbose and len(self.rank_mislabels) > 0:
print(DISCLAIMER, "\n")
print("Mislabeled sequences:\n")
print(header)
def write_rank_mislabels(self, final=True):
if not self.cfg.ranktest:
return
with open(self.misrank_fname, "w") as fo_all:
fields = [
"RankID", "MislabeledLevel", "OriginalLabel", "ProposedLabel",
"Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath",
"PerRankConfidence"
]
self.write_mislabels_header(fo_all, final, fields)
for mis_rec in self.rank_mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose:
print(output)
def write_mislabels(self, final=True):
if final:
out_fname = self.mis_fname
else:
out_fname = self.premis_fname
with open(out_fname, "w") as fo_all:
fields = [
"SeqID", "MislabeledLevel", "OriginalLabel", "ProposedLabel",
"Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath",
"PerRankConfidence"
]
if self.cfg.ranktest:
fields += ["HigherRankMisplacedConfidence"]
self.write_mislabels_header(fo_all, final, fields)
for mis_rec in self.mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose and final:
print(output)
if final:
self.write_rank_mislabels()
self.write_stats()
def get_parent_tip_ranks(self, tax_tree):
rank_tips = {}
rank_parent = {}
for node in tax_tree.traverse("postorder"):
if node.is_leaf() or node.is_root():
continue
tax_path = node.name
ranks = Taxonomy.split_rank_uid(tax_path)
rank_lvl = Taxonomy.lowest_assigned_rank_level(ranks)
if rank_lvl < 2:
continue
parent_ranks = Taxonomy.split_rank_uid(node.up.name)
parent_lvl = Taxonomy.lowest_assigned_rank_level(parent_ranks)
if parent_lvl < 1:
continue
rank_seqs = node.get_leaf_names()
rank_size = len(rank_seqs)
if rank_size < 2 or rank_size > self.reftree_size - 4:
continue
# print rank_lvl, "\t", tax_path, "\t", rank_seqs, "\n"
rank_tips[tax_path] = node.get_leaf_names()
rank_parent[tax_path] = parent_ranks
return rank_parent, rank_tips
def run_leave_subtree_out_test(self):
job_name = self.cfg.subst_name("l1out_rank_%NAME%")
# if self.jplace_fname:
# jp = EpaJsonParser(self.jplace_fname)
# else:
#create file with subtrees
rank_parent, rank_tips = self.get_parent_tip_ranks(self.tax_tree)
subtree_list = list(rank_tips.items())
if len(subtree_list) == 0:
return 0
subtree_list_file = self.cfg.tmp_fname("treelist_%NAME%.txt")
with open(subtree_list_file, "w") as fout:
for rank_name, tips in subtree_list:
fout.write("%s\n" % " ".join(tips))
jp_list = self.raxml.run_epa(job_name,
self.refalign_fname,
self.reftree_fname,
self.optmod_fname,
mode="l1o_subtree",
subtree_fname=subtree_list_file)
subtree_count = 0
for jp in jp_list:
placements = jp.get_placement()
for place in placements:
ranks, lws = self.classify_seq(place)
tax_path = subtree_list[subtree_count][0]
orig_ranks = Taxonomy.split_rank_uid(tax_path)
rank_level = Taxonomy.lowest_assigned_rank_level(orig_ranks)
rank_prefix = self.tax_code.guess_rank_level_name(
orig_ranks, rank_level)[0]
rank_name = orig_ranks[rank_level]
if not rank_name.startswith(rank_prefix):
rank_name = rank_prefix + rank_name
parent_ranks = rank_parent[tax_path]
# print orig_ranks, "\n", parent_ranks, "\n", ranks, "\n"
mis_rec = self.check_rank_tax_labels(rank_name, parent_ranks,
ranks, lws)
if mis_rec:
self.misrank_conf_map[tax_path] = mis_rec['conf']
subtree_count += 1
return subtree_count
def run_leave_seq_out_test(self):
job_name = self.cfg.subst_name("l1out_seq_%NAME%")
placements = []
if self.cfg.jplace_fname:
if os.path.isdir(self.cfg.jplace_fname):
jplace_fmask = os.path.join(self.cfg.jplace_fname, '*.jplace')
else:
jplace_fmask = self.cfg.jplace_fname
jplace_fname_list = glob.glob(jplace_fmask)
for jplace_fname in jplace_fname_list:
jp = EpaJsonParser(jplace_fname)
placements += jp.get_placement()
config.log.debug("Loaded %d placements from %s\n", len(placements),
jplace_fmask)
else:
jp = self.raxml.run_epa(job_name,
self.refalign_fname,
self.reftree_fname,
self.optmod_fname,
mode="l1o_seq")
placements = jp.get_placement()
if self.cfg.output_interim_files:
out_jplace_fname = self.cfg.out_fname(
"%NAME%.l1out_seq.jplace")
self.raxml.copy_epa_jplace(job_name,
out_jplace_fname,
move=True,
mode="l1o_seq")
seq_count = 0
l1out_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
# orig_ranks = self.get_orig_ranks(seq_name)
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# get EPA tax label
ranks, lws = self.classify_seq(place)
l1out_ass[seq_name] = (ranks, lws)
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks,
lws)
# cross-check with higher rank mislabels
if self.cfg.ranktest and mis_rec:
rank_conf = 0
for lvl in range(2, len(orig_ranks)):
tax_path = Taxonomy.get_rank_uid(orig_ranks, lvl)
if tax_path in self.misrank_conf_map:
rank_conf = max(rank_conf,
self.misrank_conf_map[tax_path])
mis_rec['rank_conf'] = rank_conf
seq_count += 1
self.write_assignments(l1out_ass, final=False)
return seq_count
def prune_mislabels_from_tree(self, src_tree, tree_name):
pruned_tree = src_tree.copy(method="newick")
name2node = {}
for leaf in pruned_tree.iter_leaves():
name2node[leaf.name] = leaf
for mis_rec in self.mislabels:
rname = mis_rec['name']
# rname = EpacConfig.REF_SEQ_PREFIX + name
if rname in name2node:
name2node[rname].delete()
else:
config.log.debug("Node not found in the %s tree: %s" %
(tree_name, rname))
return pruned_tree
def run_final_epa_test(self):
self.reftree_outgroup = self.refjson.get_outgroup()
pruned_reftree = self.prune_mislabels_from_tree(
self.reftree, "reference")
pruned_taxtree = self.prune_mislabels_from_tree(
self.reftree, "taxonomic")
# remove unifurcation at the root
if len(pruned_reftree.children) == 1:
pruned_reftree = pruned_reftree.children[0]
self.mislabels = []
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(pruned_taxtree)
reftree_epalbl_str = None
if self.cfg.final_jplace_fname:
if os.path.isdir(self.cfg.final_jplace_fname):
jplace_fmask = os.path.join(self.cfg.final_jplace_fname,
'*.jplace')
else:
jplace_fmask = self.cfg.final_jplace_fname
jplace_fname_list = glob.glob(jplace_fmask)
placements = []
for jplace_fname in jplace_fname_list:
jp = EpaJsonParser(jplace_fname)
placements += jp.get_placement()
if not reftree_epalbl_str:
reftree_epalbl_str = jp.get_std_newick_tree()
config.log.debug("Loaded %d final epa placements from %s\n",
len(placements), jplace_fmask)
else:
epa_result = self.run_epa_once(pruned_reftree)
reftree_epalbl_str = epa_result.get_std_newick_tree()
placements = epa_result.get_placement()
# update branchid-taxonomy mapping to account for possible changes in branch numbering
reftree_tax = Tree(reftree_epalbl_str)
th.set_bf_unrooted_tree(reftree_tax)
bid_tax_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(bid_tax_map, final=True)
cl = TaxClassifyHelper(self.cfg, bid_tax_map, self.rate,
self.node_height)
# newtax_fname = self.cfg.subst_name("newtax_%NAME%.tre")
# th.get_tax_tree().write(outfile=newtax_fname, format=3)
final_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# EXPERIMENTAL FEATURE - disabled for now!
# It could happen that certain ranks were present in the "original" reference tree, but
# are completely missing in the pruned tree (e.g., all seqs of a species were considered "suspicious"
# after the leave-one-out test and thus pruned)
# In this case, EPA has no chance to infer full original taxonomic annotation (=species) since the corresponding clade
# is now missing. To account for this fact, we amend the original taxonomic annotation and set ranks missing from
# pruned tree to "Undefined".
# orig_ranks = th.strip_missing_ranks(orig_ranks)
# print orig_ranks
# get EPA tax label
ranks, lws = cl.classify_seq(place["p"])
final_ass[seq_name] = (ranks, lws)
#print seq_name, ": ", orig_ranks, "--->", ranks
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks,
lws)
self.write_assignments(final_ass, final=True)
def run_epa_once(self, reftree):
reftree_fname = self.cfg.tmp_fname("final_ref_%NAME%.tre")
job_name = self.cfg.subst_name("final_epa_%NAME%")
reftree.write(outfile=reftree_fname)
# IMPORTANT: don't load the model, since it's invalid for the pruned true !!!
optmod_fname = ""
epa_result = self.raxml.run_epa(job_name, self.refalign_fname,
reftree_fname, optmod_fname)
if self.cfg.output_interim_files:
out_jplace_fname = self.cfg.out_fname("%NAME%.final_epa.jplace")
self.raxml.copy_epa_jplace(job_name, out_jplace_fname, move=True)
return epa_result
def run_test(self):
self.raxml = RaxmlWrapper(self.cfg)
# config.log.info("Number of sequences in the reference: %d\n", self.reftree_size)
self.refjson.get_raxml_readable_tree(self.reftree_fname)
self.refalign_fname = self.refjson.get_alignment(self.tmp_refaln)
self.refjson.get_binary_model(self.optmod_fname)
if self.cfg.ranktest:
config.log.info("Running the leave-one-rank-out test...\n")
subtree_count = self.run_leave_subtree_out_test()
config.log.info("Running the leave-one-sequence-out test...\n")
self.run_leave_seq_out_test()
if len(self.mislabels) > 0:
config.log.info(
"Leave-one-out test identified %d suspicious sequences; running final EPA test to check them...\n",
len(self.mislabels))
if self.cfg.debug:
self.write_mislabels(final=False)
self.run_final_epa_test()
self.filter_mislabels()
self.sort_mislabels()
self.write_mislabels()
config.log.info("\nTotal mislabels: %d / %.2f %%", len(self.mislabels),
(float(len(self.mislabels)) / self.reftree_size * 100))
def run_final_epa_test(self):
self.reftree_outgroup = self.refjson.get_outgroup()
pruned_reftree = self.prune_mislabels_from_tree(
self.reftree, "reference")
pruned_taxtree = self.prune_mislabels_from_tree(
self.reftree, "taxonomic")
# remove unifurcation at the root
if len(pruned_reftree.children) == 1:
pruned_reftree = pruned_reftree.children[0]
self.mislabels = []
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(pruned_taxtree)
reftree_epalbl_str = None
if self.cfg.final_jplace_fname:
if os.path.isdir(self.cfg.final_jplace_fname):
jplace_fmask = os.path.join(self.cfg.final_jplace_fname,
'*.jplace')
else:
jplace_fmask = self.cfg.final_jplace_fname
jplace_fname_list = glob.glob(jplace_fmask)
placements = []
for jplace_fname in jplace_fname_list:
jp = EpaJsonParser(jplace_fname)
placements += jp.get_placement()
if not reftree_epalbl_str:
reftree_epalbl_str = jp.get_std_newick_tree()
config.log.debug("Loaded %d final epa placements from %s\n",
len(placements), jplace_fmask)
else:
epa_result = self.run_epa_once(pruned_reftree)
reftree_epalbl_str = epa_result.get_std_newick_tree()
placements = epa_result.get_placement()
# update branchid-taxonomy mapping to account for possible changes in branch numbering
reftree_tax = Tree(reftree_epalbl_str)
th.set_bf_unrooted_tree(reftree_tax)
bid_tax_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(bid_tax_map, final=True)
cl = TaxClassifyHelper(self.cfg, bid_tax_map, self.rate,
self.node_height)
# newtax_fname = self.cfg.subst_name("newtax_%NAME%.tre")
# th.get_tax_tree().write(outfile=newtax_fname, format=3)
final_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# EXPERIMENTAL FEATURE - disabled for now!
# It could happen that certain ranks were present in the "original" reference tree, but
# are completely missing in the pruned tree (e.g., all seqs of a species were considered "suspicious"
# after the leave-one-out test and thus pruned)
# In this case, EPA has no chance to infer full original taxonomic annotation (=species) since the corresponding clade
# is now missing. To account for this fact, we amend the original taxonomic annotation and set ranks missing from
# pruned tree to "Undefined".
# orig_ranks = th.strip_missing_ranks(orig_ranks)
# print orig_ranks
# get EPA tax label
ranks, lws = cl.classify_seq(place["p"])
final_ass[seq_name] = (ranks, lws)
#print seq_name, ": ", orig_ranks, "--->", ranks
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks,
lws)
self.write_assignments(final_ass, final=True)
class LeaveOneTest:
def __init__(self, config, args):
self.cfg = config
self.method = args.method
self.minlw = args.min_lhw
self.jplace_fname = args.jplace_fname
self.ranktest = args.ranktest
self.output_fname = args.output_dir + "/" + args.output_name
# switch off branch length filter
self.brlen_pv = 0.
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
self.reftree_lbl_fname = config.tmp_fname("%NAME%_lbl.tre")
self.reftree_tax_fname = config.tmp_fname("%NAME%_tax.tre")
self.optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
try:
self.refjson = RefJsonParser(config.refjson_fname, ver="1.2")
except ValueError:
print("Invalid json file format!")
sys.exit()
#validate input json format
self.refjson.validate()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.bid_taxonomy_map = self.refjson.get_bid_tanomomy_map()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.brlen_pv, self.rate, self.node_height)
self.TAXONOMY_RANKS_COUNT = 10
self.mislabels = []
self.mislabels_cnt = [0] * self.TAXONOMY_RANKS_COUNT
self.rank_mislabels = []
self.rank_mislabels_cnt = [0] * self.TAXONOMY_RANKS_COUNT
self.misrank_conf_map = {}
def cleanup(self):
FileUtils.remove_if_exists(self.tmp_refaln)
def classify_seq(self, placement):
edges = placement["p"]
if len(edges) > 0:
return self.classify_helper.classify_seq(edges, self.method, self.minlw)
else:
print "ERROR: no placements! something is definitely wrong!"
def rank_level_name(self, uni_rank_level):
return { 0: ("?__", "Unknown"),
1: ("k__", "Kingdom"),
2: ("p__", "Phylum"),
3: ("c__", "Class"),
4: ("d__", "Subclass"),
5: ("o__", "Order"),
6: ("n__", "Suborder"),
7: ("f__", "Family"),
8: ("g__", "Genus"),
9: ("s__", "Species")
}[uni_rank_level]
def guess_rank_level(self, ranks, rank_level):
rank_name = ranks[rank_level]
real_level = 0
# check common prefixes and suffixes
if rank_name.startswith("k__") or rank_name.lower() in ["bacteria", "archaea", "eukaryota"]:
real_level = 1
elif rank_name.startswith("p__"):
real_level = 2
elif rank_name.startswith("c__"):
real_level = 3
elif rank_name.endswith("dae"):
real_level = 4
elif rank_name.startswith("o__") or rank_name.endswith("ales"):
real_level = 5
elif rank_name.endswith("neae"):
real_level = 6
elif rank_name.startswith("f__") or rank_name.endswith("ceae"):
real_level = 7
elif rank_name.startswith("g__"):
real_level = 8
elif rank_name.startswith("s__"):
real_level = 9
if real_level == 0:
if rank_level == 0: # kingdom
real_level = 1
else:
parent_level = self.guess_rank_level(ranks, rank_level-1)
real_level = parent_level + 1
if len(ranks) < 8 and (real_level in [4,6]):
real_level += 1
return real_level
def guess_rank_level_name(self, ranks, rank_level):
real_level = self.guess_rank_level(ranks, rank_level)
return self.rank_level_name(real_level)
def check_seq_tax_labels(self, seq_name, orig_ranks, ranks, lws):
mislabel_lvl = -1
min_len = min(len(orig_ranks),len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.guess_rank_level(orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = EpacConfig.strip_ref_prefix(seq_name)
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.rank_level_name(real_lvl)[1]
mis_rec['inv_level'] = -1 * real_lvl # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
self.mislabels.append(mis_rec)
return mis_rec
else:
return None
def check_rank_tax_labels(self, rank_name, orig_ranks, ranks, lws):
mislabel_lvl = -1
min_len = min(len(orig_ranks),len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.guess_rank_level(orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = rank_name
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.rank_level_name(real_lvl)[1]
mis_rec['inv_level'] = -1 * real_lvl # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
self.rank_mislabels.append(mis_rec)
return mis_rec
else:
return None
def mis_rec_to_string_old(self, mis_rec):
lvl = mis_rec['orig_level']
output = mis_rec['name'] + "\t"
output += "%s\t%s\t%s\t%.3f\n" % (mis_rec['level_name'],
mis_rec['orig_ranks'][lvl], mis_rec['ranks'][lvl], mis_rec['lws'][lvl])
output += ";".join(mis_rec['orig_ranks']) + "\n"
output += ";".join(mis_rec['ranks']) + "\n"
output += "\t".join(["%.3f" % conf for conf in mis_rec['lws']]) + "\n"
return output
def mis_rec_to_string(self, mis_rec):
lvl = mis_rec['orig_level']
output = mis_rec['name'] + "\t"
output += "%s\t%s\t%s\t%.3f\t" % (mis_rec['level_name'],
mis_rec['orig_ranks'][lvl], mis_rec['ranks'][lvl], mis_rec['lws'][lvl])
output += Taxonomy.lineage_str(mis_rec['orig_ranks']) + "\t"
output += Taxonomy.lineage_str(mis_rec['ranks']) + "\t"
output += ";".join(["%.3f" % conf for conf in mis_rec['lws']])
if 'rank_conf' in mis_rec:
output += "\t%.3f" % mis_rec['rank_conf']
return output
def sort_mislabels(self):
self.mislabels = sorted(self.mislabels, key=itemgetter('inv_level', 'conf'), reverse=True)
for mis_rec in self.mislabels:
real_lvl = mis_rec["real_level"]
self.mislabels_cnt[real_lvl] += 1
if self.ranktest:
self.rank_mislabels = sorted(self.rank_mislabels, key=itemgetter('inv_level', 'conf'), reverse=True)
for mis_rec in self.rank_mislabels:
real_lvl = mis_rec["real_level"]
self.rank_mislabels_cnt[real_lvl] += 1
def write_mislabels(self, final=True):
if final:
out_fname = "%s.mis" % self.output_fname
else:
out_fname = "%s.premis" % self.output_fname
with open(out_fname, "w") as fo_all:
fields = ["SeqID", "MislabeledLevel", "OriginalLabel", "ProposedLabel", "Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath", "PerRankConfidence"]
if self.ranktest:
fields += ["HigherRankMisplacedConfidence"]
header = ";" + "\t".join(fields) + "\n"
fo_all.write(header)
if self.cfg.verbose and len(self.mislabels) > 0 and final:
print "Mislabeled sequences:\n"
print header
for mis_rec in self.mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose and final:
print(output)
if not final:
return
if self.ranktest:
with open("%s.misrank" % self.output_fname, "w") as fo_all:
fields = ["RankID", "MislabeledLevel", "OriginalLabel", "ProposedLabel", "Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath", "PerRankConfidence"]
header = ";" + "\t".join(fields) + "\n"
fo_all.write(header)
if self.cfg.verbose and len(self.rank_mislabels) > 0:
print "\nMislabeled higher ranks:\n"
print header
for mis_rec in self.rank_mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose:
print(output)
print "Mislabels counts by ranks:"
with open("%s.stats" % self.output_fname, "w") as fo_stat:
seq_sum = 0
rank_sum = 0
for i in range(1, self.TAXONOMY_RANKS_COUNT):
rname = self.rank_level_name(i)[1].ljust(10)
if self.mislabels_cnt[i] > 0 or i not in [4,6]:
seq_sum += self.mislabels_cnt[i]
output = "%s:\t%d" % (rname, seq_sum)
if self.ranktest:
rank_sum += self.rank_mislabels_cnt[i]
output += "\t%d" % rank_sum
fo_stat.write(output + "\n")
print(output)
def get_orig_ranks(self, seq_name):
nodes = self.tax_tree.get_leaves_by_name(seq_name)
if len(nodes) != 1:
print "FATAL ERROR: Sequence %s is not found in the taxonomic tree, or is present more than once!" % seq_name
sys.exit()
seq_node = nodes[0]
orig_ranks = Taxonomy.split_rank_uid(seq_node.up.name)
return orig_ranks
def run_leave_subtree_out_test(self):
job_name = self.cfg.subst_name("l1out_rank_%NAME%")
# if self.jplace_fname:
# jp = EpaJsonParser(self.jplace_fname)
# else:
#create file with subtrees
rank_tips = {}
rank_parent = {}
for node in self.tax_tree.traverse("postorder"):
if node.is_leaf() or node.is_root():
continue
tax_path = node.name
ranks = Taxonomy.split_rank_uid(tax_path)
rank_lvl = Taxonomy.lowest_assigned_rank_level(ranks)
if rank_lvl < 2:
continue
parent_ranks = Taxonomy.split_rank_uid(node.up.name)
parent_lvl = Taxonomy.lowest_assigned_rank_level(parent_ranks)
if parent_lvl < 1:
continue
rank_seqs = node.get_leaf_names()
rank_size = len(rank_seqs)
if rank_size < 2 or rank_size > self.reftree_size-4:
continue
# print rank_lvl, "\t", tax_path, "\t", rank_seqs, "\n"
rank_tips[tax_path] = node.get_leaf_names()
rank_parent[tax_path] = parent_ranks
subtree_list = rank_tips.items()
if len(subtree_list) == 0:
return 0
subtree_list_file = self.cfg.tmp_fname("treelist_%NAME%.txt")
with open(subtree_list_file, "w") as fout:
for rank_name, tips in subtree_list:
fout.write("%s\n" % " ".join(tips))
jp_list = self.raxml.run_epa(job_name, self.refalign_fname, self.reftree_fname, self.optmod_fname,
mode="l1o_subtree", subtree_fname=subtree_list_file)
subtree_count = 0
for jp in jp_list:
placements = jp.get_placement()
for place in placements:
ranks, lws = self.classify_seq(place)
tax_path = subtree_list[subtree_count][0]
orig_ranks = Taxonomy.split_rank_uid(tax_path)
rank_level = Taxonomy.lowest_assigned_rank_level(orig_ranks)
rank_prefix = self.guess_rank_level_name(orig_ranks, rank_level)[0]
rank_name = orig_ranks[rank_level]
if not rank_name.startswith(rank_prefix):
rank_name = rank_prefix + rank_name
parent_ranks = rank_parent[tax_path]
# print orig_ranks, "\n", parent_ranks, "\n", ranks, "\n"
mis_rec = self.check_rank_tax_labels(rank_name, parent_ranks, ranks, lws)
if mis_rec:
self.misrank_conf_map[tax_path] = mis_rec['conf']
subtree_count += 1
return subtree_count
def run_leave_seq_out_test(self):
job_name = self.cfg.subst_name("l1out_seq_%NAME%")
if self.jplace_fname:
jp = EpaJsonParser(self.jplace_fname)
else:
jp = self.raxml.run_epa(job_name, self.refalign_fname, self.reftree_fname, self.optmod_fname, mode="l1o_seq")
placements = jp.get_placement()
seq_count = 0
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.get_orig_ranks(seq_name)
# get EPA tax label
ranks, lws = self.classify_seq(place)
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks, lws)
# cross-check with higher rank mislabels
if self.ranktest and mis_rec:
rank_conf = 0
for lvl in range(2,len(orig_ranks)):
tax_path = Taxonomy.get_rank_uid(orig_ranks, lvl)
if tax_path in self.misrank_conf_map:
rank_conf = max(rank_conf, self.misrank_conf_map[tax_path])
mis_rec['rank_conf'] = rank_conf
seq_count += 1
return seq_count
def run_final_epa_test(self):
self.reftree_outgroup = self.refjson.get_outgroup()
tmp_reftree = self.reftree.copy()
tmp_taxtree = self.tax_tree.copy()
for mis_rec in self.mislabels:
name = mis_rec['name']
rname = EpacConfig.REF_SEQ_PREFIX + name
leaf_nodes = tmp_reftree.get_leaves_by_name(rname)
if len(leaf_nodes) > 0:
leaf_nodes[0].delete()
else:
print "Node not found in the reference tree: %s" % rname
leaf_nodes = tmp_taxtree.get_leaves_by_name(rname)
if len(leaf_nodes) > 0:
leaf_nodes[0].delete()
else:
print "Node not found in the taxonomic tree: %s" % rname
# remove unifurcation at the root
if len(tmp_reftree.children) == 1:
tmp_reftree = tmp_reftree.children[0]
self.mislabels = []
th = TaxTreeHelper(self.origin_taxonomy, self.cfg)
th.set_mf_rooted_tree(tmp_taxtree)
self.run_epa_once(tmp_reftree, th)
def run_epa_once(self, reftree, th):
reftree_fname = self.cfg.tmp_fname("final_ref_%NAME%.tre")
job_name = self.cfg.subst_name("final_epa_%NAME%")
reftree.write(outfile=reftree_fname)
# IMPORTANT: don't load the model, since it's invalid for the pruned true !!!
optmod_fname=""
epa_result = self.raxml.run_epa(job_name, self.refalign_fname, reftree_fname, optmod_fname)
reftree_epalbl_str = epa_result.get_std_newick_tree()
placements = epa_result.get_placement()
# update branchid-taxonomy mapping to account for possible changes in branch numbering
reftree_tax = Tree(reftree_epalbl_str)
th.set_bf_unrooted_tree(reftree_tax)
bid_tax_map = th.get_bid_taxonomy_map()
cl = TaxClassifyHelper(self.cfg, bid_tax_map, self.brlen_pv, self.rate, self.node_height)
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.get_orig_ranks(seq_name)
# get EPA tax label
ranks, lws = cl.classify_seq(place["p"])
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks, lws)
if not self.cfg.debug:
self.raxml.cleanup(job_name)
FileUtils.remove_if_exists(reftree_fname)
def run_test(self):
self.raxml = RaxmlWrapper(self.cfg)
print "Number of sequences in the reference: %d\n" % self.reftree_size
self.refjson.get_raxml_readable_tree(self.reftree_fname)
self.refalign_fname = self.refjson.get_alignment(self.tmp_refaln)
self.refjson.get_binary_model(self.optmod_fname)
if self.ranktest:
print "Running the leave-one-rank-out test...\n"
subtree_count = self.run_leave_subtree_out_test()
print "Running the leave-one-sequence-out test...\n"
self.run_leave_seq_out_test()
if len(self.mislabels) > 0:
print "Leave-one-out test identified %d suspicious sequences; running final EPA test to check them...\n" % len(self.mislabels)
self.write_mislabels(final=False)
self.run_final_epa_test()
self.sort_mislabels()
self.write_mislabels()
print "\nPercentage of mislabeled sequences: %.2f %%" % (float(len(self.mislabels)) / self.reftree_size * 100)
if not self.cfg.debug:
FileUtils.remove_if_exists(self.reftree_fname)
FileUtils.remove_if_exists(self.optmod_fname)
FileUtils.remove_if_exists(self.refalign_fname)
class LeaveOneTest:
def __init__(self, config):
self.cfg = config
self.mis_fname = self.cfg.out_fname("%NAME%.mis")
self.premis_fname = self.cfg.out_fname("%NAME%.premis")
self.misrank_fname = self.cfg.out_fname("%NAME%.misrank")
self.stats_fname = self.cfg.out_fname("%NAME%.stats")
if os.path.isfile(self.mis_fname):
print "\nERROR: Output file already exists: %s" % self.mis_fname
print "Please specify a different job name using -n or remove old output files."
self.cfg.exit_user_error()
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
self.reftree_lbl_fname = config.tmp_fname("%NAME%_lbl.tre")
self.reftree_tax_fname = config.tmp_fname("%NAME%_tax.tre")
self.optmod_fname = self.cfg.tmp_fname("%NAME%.opt")
self.reftree_fname = self.cfg.tmp_fname("ref_%NAME%.tre")
self.mislabels = []
self.mislabels_cnt = []
self.rank_mislabels = []
self.rank_mislabels_cnt = []
self.misrank_conf_map = {}
def write_bid_tax_map(self, bid_tax_map, final):
if self.cfg.debug:
fname_suffix = "final" if final else "l1out"
bid_fname = self.cfg.tmp_fname("%NAME%_" + "bid_tax_map_%s.txt" % fname_suffix)
with open(bid_fname, "w") as outf:
for bid, bid_rec in bid_tax_map.iteritems():
outf.write("%s\t%s\t%d\t%f\n" % (bid, bid_rec[0], bid_rec[1], bid_rec[2]));
def write_assignments(self, assign_map, final):
if self.cfg.debug:
fname_suffix = "final" if final else "l1out"
assign_fname = self.cfg.tmp_fname("%NAME%_" + "taxassign_%s.txt" % fname_suffix)
with open(assign_fname, "w") as outf:
for seq_name in assign_map.iterkeys():
ranks, lws = assign_map[seq_name]
outf.write("%s\t%s\t%s\n" % (seq_name, ";".join(ranks), ";".join(["%.3f" % l for l in lws])))
def load_refjson(self, refjson_fname):
try:
self.refjson = RefJsonParser(refjson_fname)
except ValueError:
self.cfg.exit_user_error("ERROR: Invalid json file format!")
#validate input json format
(valid, err) = self.refjson.validate()
if not valid:
self.cfg.log.error("ERROR: Parsing reference JSON file failed:\n%s", err)
self.cfg.exit_user_error()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.origin_taxonomy = self.refjson.get_origin_taxonomy()
self.tax_tree = self.refjson.get_tax_tree()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.bid_taxonomy_map = self.refjson.get_branch_tax_map()
if not self.bid_taxonomy_map:
# old file format (before 1.6), need to rebuild this map from scratch
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(self.tax_tree)
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(self.bid_taxonomy_map, final=False)
reftree_str = self.refjson.get_raxml_readable_tree()
self.reftree = Tree(reftree_str)
self.reftree_size = len(self.reftree.get_leaves())
# IMPORTANT: set EPA heuristic rate based on tree size!
self.cfg.resolve_auto_settings(self.reftree_size)
# If we're loading the pre-optimized model, we MUST set the same rate het. mode as in the ref file
if self.cfg.epa_load_optmod:
self.cfg.raxml_model = self.refjson.get_ratehet_model()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.rate, self.node_height)
self.taxtree_helper = TaxTreeHelper(self.cfg, self.origin_taxonomy, self.tax_tree)
tax_code_name = self.refjson.get_taxcode()
self.tax_code = TaxCode(tax_code_name)
self.taxonomy = Taxonomy(prefix=EpacConfig.REF_SEQ_PREFIX, tax_map=self.origin_taxonomy)
self.tax_common_ranks = self.taxonomy.get_common_ranks()
# print "Common ranks: ", self.tax_common_ranks
self.mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
self.rank_mislabels_cnt = [0] * TaxCode.UNI_TAX_LEVELS
def run_epa_trainer(self):
epa_trainer.run_trainer(self.cfg)
if not os.path.isfile(self.cfg.refjson_fname):
self.cfg.log.error("\nBuilding reference tree failed, see error messages above.")
self.cfg.exit_fatal_error()
def classify_seq(self, placement):
edges = placement["p"]
if len(edges) > 0:
return self.classify_helper.classify_seq(edges)
else:
print "ERROR: no placements! something is definitely wrong!"
def check_seq_tax_labels(self, seq_name, orig_ranks, ranks, lws):
mis_rec = None
num_common_ranks = len(self.tax_common_ranks)
orig_rank_level = Taxonomy.lowest_assigned_rank_level(orig_ranks)
new_rank_level = Taxonomy.lowest_assigned_rank_level(ranks)
#if new_rank_level < 0 or (new_rank_level < num_common_ranks and orig_rank_level >= num_common_ranks):
# if new_rank_level < 0:
if len(ranks) == 0:
mis_rec = {}
mis_rec['name'] = seq_name
mis_rec['orig_level'] = -1
mis_rec['real_level'] = 0
mis_rec['level_name'] = "[NotIngroup]"
mis_rec['inv_level'] = -1 * mis_rec['real_level'] # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = []
mis_rec['lws'] = [1.0]
mis_rec['conf'] = mis_rec['lws'][0]
else:
mislabel_lvl = -1
min_len = min(len(orig_ranks),len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.tax_code.guess_rank_level(orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = seq_name
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.tax_code.rank_level_name(real_lvl)[0]
mis_rec['inv_level'] = -1 * mis_rec['real_level'] # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
if mis_rec:
self.mislabels.append(mis_rec)
return mis_rec
def filter_mislabels(self):
filtered_mis = []
for i in range(len(self.mislabels)):
if self.mislabels[i]['conf'] >= self.cfg.conf_cutoff:
filtered_mis.append(self.mislabels[i])
self.mislabels = filtered_mis
def check_rank_tax_labels(self, rank_name, orig_ranks, ranks, lws):
mislabel_lvl = -1
min_len = min(len(orig_ranks),len(ranks))
for rank_lvl in range(min_len):
if ranks[rank_lvl] != Taxonomy.EMPTY_RANK and ranks[rank_lvl] != orig_ranks[rank_lvl]:
mislabel_lvl = rank_lvl
break
if mislabel_lvl >= 0:
real_lvl = self.tax_code.guess_rank_level(orig_ranks, mislabel_lvl)
mis_rec = {}
mis_rec['name'] = rank_name
mis_rec['orig_level'] = mislabel_lvl
mis_rec['real_level'] = real_lvl
mis_rec['level_name'] = self.tax_code.rank_level_name(real_lvl)[0]
mis_rec['inv_level'] = -1 * real_lvl # just for sorting
mis_rec['orig_ranks'] = orig_ranks
mis_rec['ranks'] = ranks
mis_rec['lws'] = lws
mis_rec['conf'] = lws[mislabel_lvl]
self.rank_mislabels.append(mis_rec)
return mis_rec
else:
return None
def mis_rec_to_string_old(self, mis_rec):
lvl = mis_rec['orig_level']
output = mis_rec['name'] + "\t"
output += "%s\t%s\t%s\t%.3f\n" % (mis_rec['level_name'],
mis_rec['orig_ranks'][lvl], mis_rec['ranks'][lvl], mis_rec['lws'][lvl])
output += ";".join(mis_rec['orig_ranks']) + "\n"
output += ";".join(mis_rec['ranks']) + "\n"
output += "\t".join(["%.3f" % conf for conf in mis_rec['lws']]) + "\n"
return output
def mis_rec_to_string(self, mis_rec):
lvl = mis_rec['orig_level']
uncorr_name = EpacConfig.strip_ref_prefix(self.refjson.get_uncorr_seqid(mis_rec['name']))
uncorr_orig_ranks = self.refjson.get_uncorr_ranks(mis_rec['orig_ranks'])
uncorr_ranks = self.refjson.get_uncorr_ranks(mis_rec['ranks'])
output = uncorr_name + "\t"
if lvl >= 0:
output += "%s\t%s\t%s\t%.3f\t" % (mis_rec['level_name'],
uncorr_orig_ranks[lvl], uncorr_ranks[lvl], mis_rec['lws'][lvl])
else:
output += "%s\t%s\t%s\t%.3f\t" % (mis_rec['level_name'],
"NA", "NA", mis_rec['lws'][0])
output += Taxonomy.lineage_str(uncorr_orig_ranks) + "\t"
output += Taxonomy.lineage_str(uncorr_ranks) + "\t"
output += ";".join(["%.3f" % conf for conf in mis_rec['lws']])
if 'rank_conf' in mis_rec:
output += "\t%.3f" % mis_rec['rank_conf']
return output
def sort_mislabels(self):
self.mislabels = sorted(self.mislabels, key=itemgetter('inv_level', 'conf', 'name'), reverse=True)
for mis_rec in self.mislabels:
real_lvl = mis_rec["real_level"]
self.mislabels_cnt[real_lvl] += 1
if self.cfg.ranktest:
self.rank_mislabels = sorted(self.rank_mislabels, key=itemgetter('inv_level', 'conf', 'name'), reverse=True)
for mis_rec in self.rank_mislabels:
real_lvl = mis_rec["real_level"]
self.rank_mislabels_cnt[real_lvl] += 1
def write_stats(self, toFile=False):
self.cfg.log.info("Mislabeled sequences by rank:")
seq_sum = 0
rank_sum = 0
stats = []
for i in range(len(self.mislabels_cnt)):
if i > 0:
rname = self.tax_code.rank_level_name(i)[0].ljust(12)
else:
rname = "[NotIngroup]"
if self.mislabels_cnt[i] > 0:
seq_sum += self.mislabels_cnt[i]
# output = "%s:\t%d" % (rname, seq_sum)
output = "%s:\t%d" % (rname, self.mislabels_cnt[i])
if self.cfg.ranktest:
rank_sum += self.rank_mislabels_cnt[i]
output += "\t%d" % rank_sum
self.cfg.log.info(output)
stats.append(output)
if toFile:
with open(self.stats_fname, "w") as fo_stat:
for line in stats:
fo_stat.write(line + "\n")
def write_mislabels(self, final=True):
if final:
out_fname = self.mis_fname
else:
out_fname = self.premis_fname
with open(out_fname, "w") as fo_all:
fields = ["SeqID", "MislabeledLevel", "OriginalLabel", "ProposedLabel", "Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath", "PerRankConfidence"]
if self.cfg.ranktest:
fields += ["HigherRankMisplacedConfidence"]
header = ";" + "\t".join(fields) + "\n"
fo_all.write(header)
if self.cfg.verbose and len(self.mislabels) > 0 and final:
print "Mislabeled sequences:\n"
print header
for mis_rec in self.mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose and final:
print(output)
if not final:
return
if self.cfg.ranktest:
with open(self.misrank_fname, "w") as fo_all:
fields = ["RankID", "MislabeledLevel", "OriginalLabel", "ProposedLabel", "Confidence", "OriginalTaxonomyPath", "ProposedTaxonomyPath", "PerRankConfidence"]
header = ";" + "\t".join(fields) + "\n"
fo_all.write(header)
if self.cfg.verbose and len(self.rank_mislabels) > 0:
print "\nMislabeled higher ranks:\n"
print header
for mis_rec in self.rank_mislabels:
output = self.mis_rec_to_string(mis_rec) + "\n"
fo_all.write(output)
if self.cfg.verbose:
print(output)
self.write_stats()
def run_leave_subtree_out_test(self):
job_name = self.cfg.subst_name("l1out_rank_%NAME%")
# if self.jplace_fname:
# jp = EpaJsonParser(self.jplace_fname)
# else:
#create file with subtrees
rank_tips = {}
rank_parent = {}
for node in self.tax_tree.traverse("postorder"):
if node.is_leaf() or node.is_root():
continue
tax_path = node.name
ranks = Taxonomy.split_rank_uid(tax_path)
rank_lvl = Taxonomy.lowest_assigned_rank_level(ranks)
if rank_lvl < 2:
continue
parent_ranks = Taxonomy.split_rank_uid(node.up.name)
parent_lvl = Taxonomy.lowest_assigned_rank_level(parent_ranks)
if parent_lvl < 1:
continue
rank_seqs = node.get_leaf_names()
rank_size = len(rank_seqs)
if rank_size < 2 or rank_size > self.reftree_size-4:
continue
# print rank_lvl, "\t", tax_path, "\t", rank_seqs, "\n"
rank_tips[tax_path] = node.get_leaf_names()
rank_parent[tax_path] = parent_ranks
subtree_list = rank_tips.items()
if len(subtree_list) == 0:
return 0
subtree_list_file = self.cfg.tmp_fname("treelist_%NAME%.txt")
with open(subtree_list_file, "w") as fout:
for rank_name, tips in subtree_list:
fout.write("%s\n" % " ".join(tips))
jp_list = self.raxml.run_epa(job_name, self.refalign_fname, self.reftree_fname, self.optmod_fname,
mode="l1o_subtree", subtree_fname=subtree_list_file)
subtree_count = 0
for jp in jp_list:
placements = jp.get_placement()
for place in placements:
ranks, lws = self.classify_seq(place)
tax_path = subtree_list[subtree_count][0]
orig_ranks = Taxonomy.split_rank_uid(tax_path)
rank_level = Taxonomy.lowest_assigned_rank_level(orig_ranks)
rank_prefix = self.guess_rank_level_name(orig_ranks, rank_level)[0]
rank_name = orig_ranks[rank_level]
if not rank_name.startswith(rank_prefix):
rank_name = rank_prefix + rank_name
parent_ranks = rank_parent[tax_path]
# print orig_ranks, "\n", parent_ranks, "\n", ranks, "\n"
mis_rec = self.check_rank_tax_labels(rank_name, parent_ranks, ranks, lws)
if mis_rec:
self.misrank_conf_map[tax_path] = mis_rec['conf']
subtree_count += 1
return subtree_count
def run_leave_seq_out_test(self):
job_name = self.cfg.subst_name("l1out_seq_%NAME%")
placements = []
if self.cfg.jplace_fname:
if os.path.isdir(self.cfg.jplace_fname):
jplace_fmask = os.path.join(self.cfg.jplace_fname, '*.jplace')
else:
jplace_fmask = self.cfg.jplace_fname
jplace_fname_list = glob.glob(jplace_fmask)
for jplace_fname in jplace_fname_list:
jp = EpaJsonParser(jplace_fname)
placements += jp.get_placement()
config.log.debug("Loaded %d placements from %s\n", len(placements), jplace_fmask)
else:
jp = self.raxml.run_epa(job_name, self.refalign_fname, self.reftree_fname, self.optmod_fname, mode="l1o_seq")
placements = jp.get_placement()
if self.cfg.output_interim_files:
out_jplace_fname = self.cfg.out_fname("%NAME%.l1out_seq.jplace")
self.raxml.copy_epa_jplace(job_name, out_jplace_fname, move=True, mode="l1o_seq")
seq_count = 0
l1out_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
# orig_ranks = self.get_orig_ranks(seq_name)
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# get EPA tax label
ranks, lws = self.classify_seq(place)
l1out_ass[seq_name] = (ranks, lws)
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks, lws)
# cross-check with higher rank mislabels
if self.cfg.ranktest and mis_rec:
rank_conf = 0
for lvl in range(2,len(orig_ranks)):
tax_path = Taxonomy.get_rank_uid(orig_ranks, lvl)
if tax_path in self.misrank_conf_map:
rank_conf = max(rank_conf, self.misrank_conf_map[tax_path])
mis_rec['rank_conf'] = rank_conf
seq_count += 1
self.write_assignments(l1out_ass, final=False)
return seq_count
def run_final_epa_test(self):
self.reftree_outgroup = self.refjson.get_outgroup()
tmp_reftree = self.reftree.copy(method="newick")
name2refnode = {}
for leaf in tmp_reftree.iter_leaves():
name2refnode[leaf.name] = leaf
tmp_taxtree = self.tax_tree.copy(method="newick")
name2taxnode = {}
for leaf in tmp_taxtree.iter_leaves():
name2taxnode[leaf.name] = leaf
for mis_rec in self.mislabels:
rname = mis_rec['name']
# rname = EpacConfig.REF_SEQ_PREFIX + name
if rname in name2refnode:
name2refnode[rname].delete()
else:
print "Node not found in the reference tree: %s" % rname
if rname in name2taxnode:
name2taxnode[rname].delete()
else:
print "Node not found in the taxonomic tree: %s" % rname
# remove unifurcation at the root
if len(tmp_reftree.children) == 1:
tmp_reftree = tmp_reftree.children[0]
self.mislabels = []
th = TaxTreeHelper(self.cfg, self.origin_taxonomy)
th.set_mf_rooted_tree(tmp_taxtree)
epa_result = self.run_epa_once(tmp_reftree)
reftree_epalbl_str = epa_result.get_std_newick_tree()
placements = epa_result.get_placement()
# update branchid-taxonomy mapping to account for possible changes in branch numbering
reftree_tax = Tree(reftree_epalbl_str)
th.set_bf_unrooted_tree(reftree_tax)
bid_tax_map = th.get_bid_taxonomy_map()
self.write_bid_tax_map(bid_tax_map, final=True)
cl = TaxClassifyHelper(self.cfg, bid_tax_map, self.rate, self.node_height)
# newtax_fname = self.cfg.subst_name("newtax_%NAME%.tre")
# th.get_tax_tree().write(outfile=newtax_fname, format=3)
final_ass = {}
for place in placements:
seq_name = place["n"][0]
# get original taxonomic label
orig_ranks = self.taxtree_helper.get_seq_ranks_from_tree(seq_name)
# EXPERIMENTAL FEATURE - disabled for now!
# It could happen that certain ranks were present in the "original" reference tree, but
# are completely missing in the pruned tree (e.g., all seqs of a species were considered "suspicious"
# after the leave-one-out test and thus pruned)
# In this case, EPA has no chance to infer full original taxonomic annotation (=species) since the corresponding clade
# is now missing. To account for this fact, we amend the original taxonomic annotation and set ranks missing from
# pruned tree to "Undefined".
# orig_ranks = th.strip_missing_ranks(orig_ranks)
# print orig_ranks
# get EPA tax label
ranks, lws = cl.classify_seq(place["p"])
final_ass[seq_name] = (ranks, lws)
#print seq_name, ": ", orig_ranks, "--->", ranks
# check if they match
mis_rec = self.check_seq_tax_labels(seq_name, orig_ranks, ranks, lws)
self.write_assignments(final_ass, final=True)
def run_epa_once(self, reftree):
reftree_fname = self.cfg.tmp_fname("final_ref_%NAME%.tre")
job_name = self.cfg.subst_name("final_epa_%NAME%")
reftree.write(outfile=reftree_fname)
# IMPORTANT: don't load the model, since it's invalid for the pruned true !!!
optmod_fname=""
epa_result = self.raxml.run_epa(job_name, self.refalign_fname, reftree_fname, optmod_fname)
if self.cfg.output_interim_files:
out_jplace_fname = self.cfg.out_fname("%NAME%.final_epa.jplace")
self.raxml.copy_epa_jplace(job_name, out_jplace_fname, move=True)
return epa_result
def run_test(self):
self.raxml = RaxmlWrapper(self.cfg)
# config.log.info("Number of sequences in the reference: %d\n", self.reftree_size)
self.refjson.get_raxml_readable_tree(self.reftree_fname)
self.refalign_fname = self.refjson.get_alignment(self.tmp_refaln)
self.refjson.get_binary_model(self.optmod_fname)
if self.cfg.ranktest:
config.log.info("Running the leave-one-rank-out test...\n")
subtree_count = self.run_leave_subtree_out_test()
config.log.info("Running the leave-one-sequence-out test...\n")
self.run_leave_seq_out_test()
if len(self.mislabels) > 0:
config.log.info("Leave-one-out test identified %d suspicious sequences; running final EPA test to check them...\n", len(self.mislabels))
if self.cfg.debug:
self.write_mislabels(final=False)
self.run_final_epa_test()
self.filter_mislabels()
self.sort_mislabels()
self.write_mislabels()
config.log.info("\nTotal mislabels: %d / %.2f %%", len(self.mislabels), (float(len(self.mislabels)) / self.reftree_size * 100))