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 __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
print("Invalid json file format!")
sys.exit()
#validate input json format
self.refjson.validate()
self.bid_taxonomy_map = self.refjson.get_bid_tanomomy_map()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, args.p_value, self.rate, self.node_height)
def __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
assign_fname = args.output_name + ".assignment.txt"
self.out_assign_fname = os.path.join(args.output_dir, assign_fname)
jplace_fname = args.output_name + ".jplace"
self.out_jplace_fname = os.path.join(args.output_dir, jplace_fname)
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
self.cfg.exit_user_error("Invalid json file format: %s" %
config.refjson_fname)
#validate input json format
self.refjson.validate()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
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.refjson.get_origin_taxonomy())
th.set_mf_rooted_tree(self.refjson.get_tax_tree())
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.cfg.log.info("Loaded reference tree with %d taxa\n" %
len(self.reftree.get_leaves()))
self.classify_helper = TaxClassifyHelper(self.cfg,
self.bid_taxonomy_map,
self.rate, self.node_height)
def test_refjson_read(self):
versions = ["1.4", "1.5", "1.6"]
for ver in versions:
ref_fname = os.path.join(self.testfile_dir,
"test.refjson.v%s" % ver)
parser = RefJsonParser(ref_fname)
valid, errors = parser.validate()
self.assertTrue(valid)
self.assertEqual(parser.get_version(), ver)
jplace_fname = os.path.join(self.testfile_dir, "test.jplace")
parser = RefJsonParser(jplace_fname)
valid, errors = parser.validate()
self.assertFalse(valid)
tax_fname = os.path.join(self.testfile_dir, "test.tax")
with self.assertRaises(ValueError):
parser = RefJsonParser(tax_fname)
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 __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 __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
assign_fname = args.output_name + ".assignment.txt"
self.out_assign_fname = os.path.join(args.output_dir, assign_fname)
jplace_fname = args.output_name + ".jplace"
self.out_jplace_fname = os.path.join(args.output_dir, jplace_fname)
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
self.cfg.exit_user_error("Invalid json file format: %s" % config.refjson_fname)
#validate input json format
self.refjson.validate()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
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.refjson.get_origin_taxonomy())
th.set_mf_rooted_tree(self.refjson.get_tax_tree())
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.cfg.log.info("Loaded reference tree with %d taxa\n" % len(self.reftree.get_leaves()))
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.rate, self.node_height)
def test_refjson_read(self):
versions = ["1.4", "1.5", "1.6"]
for ver in versions:
ref_fname = os.path.join(self.testfile_dir, "test.refjson.v%s" % ver)
parser = RefJsonParser(ref_fname)
valid, errors = parser.validate()
self.assertTrue(valid)
self.assertEquals(parser.get_version(), ver)
jplace_fname = os.path.join(self.testfile_dir, "test.jplace")
parser = RefJsonParser(jplace_fname)
valid, errors = parser.validate()
self.assertFalse(valid)
tax_fname = os.path.join(self.testfile_dir, "test.tax")
with self.assertRaises(ValueError):
parser = RefJsonParser(tax_fname)
class EpaClassifier:
def __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
assign_fname = args.output_name + ".assignment.txt"
self.out_assign_fname = os.path.join(args.output_dir, assign_fname)
jplace_fname = args.output_name + ".jplace"
self.out_jplace_fname = os.path.join(args.output_dir, jplace_fname)
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
self.cfg.exit_user_error("Invalid json file format: %s" %
config.refjson_fname)
#validate input json format
self.refjson.validate()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
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.refjson.get_origin_taxonomy())
th.set_mf_rooted_tree(self.refjson.get_tax_tree())
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.cfg.log.info("Loaded reference tree with %d taxa\n" %
len(self.reftree.get_leaves()))
self.classify_helper = TaxClassifyHelper(self.cfg,
self.bid_taxonomy_map,
self.rate, self.node_height)
def require_muscle(self):
basepath = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists(basepath + "/epac/bin/muscle"):
errmsg = "The pipeline uses MUSCLE to merge alignments, please download the programm from:\n" + \
"http://www.drive5.com/muscle/downloads.htm\n" + \
"and specify path to your installation in the config file (sativa.cfg)\n"
self.cfg.exit_user_error(errmsg)
def require_hmmer(self):
basepath = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists(basepath +
"/epac/bin/hmmbuild") or not os.path.exists(
basepath + "/epac/bin/hmmalign"):
errmsg = "The pipeline uses HAMMER to align the query seqeunces, please download the programm from:\n" + \
"http://hmmer.janelia.org/\n" + \
"and specify path to your installation in the config file (sativa.cfg)\n"
self.cfg.exit_user_error(errmsg)
def align_to_refenence(self, noalign, minp=0.9):
refaln = self.refjson.get_alignment(fout=self.tmp_refaln)
fprofile = self.refjson.get_hmm_profile(self.hmmprofile)
# if there is no hmmer profile in json file, build it from scratch
if not fprofile:
hmm = hmmer(self.cfg, refaln)
fprofile = hmm.build_hmm_profile()
hm = hmmer(config=self.cfg,
refalign=refaln,
query=self.tmpquery,
refprofile=fprofile,
discard=noalign,
seqs=self.seqs,
minp=minp)
self.epa_alignment = hm.align()
def merge_alignment(self, query_seqs):
refaln = self.refjson.get_alignment_list()
with open(self.epa_alignment, "w") as fout:
for seq in refaln:
fout.write(">" + seq[0] + "\n" + seq[1] + "\n")
for name, seq, comment, sid in query_seqs.iter_entries():
fout.write(">" + name + "\n" + seq + "\n")
def write_combined_alignment(self):
self.query_count = 0
with open(self.epa_alignment, "w") as fout:
for name, seq, comment, sid in self.seqs.iter_entries():
ref_name = self.refjson.get_corr_seqid(
EpacConfig.REF_SEQ_PREFIX + name)
if ref_name in self.refjson.get_sequences_names():
seq_name = ref_name
else:
seq_name = EpacConfig.QUERY_SEQ_PREFIX + name
self.query_count += 1
fout.write(">" + seq_name + "\n" + seq + "\n")
def checkinput(self, query_fname, minp=0.9):
formats = [
"fasta", "phylip", "iphylip", "phylip_relaxed", "iphylip_relaxed"
]
for fmt in formats:
try:
self.seqs = SeqGroup(sequences=query_fname, format=fmt)
break
except:
self.cfg.log.debug("Guessing input format: not " + fmt)
if self.seqs == None:
self.cfg.exit_user_error(
"Invalid input file format: %s\nThe supported input formats are fasta and phylip"
% query_fname)
if self.ignore_refalign:
self.cfg.log.info(
"Assuming query file contains reference sequences, skipping the alignment step...\n"
)
self.write_combined_alignment()
return
self.query_count = len(self.seqs)
# add query seq name prefix to avoid confusion between reference and query sequences
self.seqs.add_name_prefix(EpacConfig.QUERY_SEQ_PREFIX)
self.seqs.write(format="fasta", outfile=self.tmpquery)
self.cfg.log.info("Checking if query sequences are aligned ...")
entries = self.seqs.get_entries()
seql = len(entries[0][1])
aligned = True
for entri in entries[1:]:
l = len(entri[1])
if not seql == l:
aligned = False
break
if aligned and len(self.seqs) > 1:
self.cfg.log.info("Query sequences are aligned")
refalnl = self.refjson.get_alignment_length()
if refalnl == seql:
self.cfg.log.info(
"Merging query alignment with reference alignment")
self.merge_alignment(self.seqs)
else:
self.cfg.log.info(
"Merging query alignment with reference alignment using MUSCLE"
)
self.require_muscle()
refaln = self.refjson.get_alignment(fout=self.tmp_refaln)
m = muscle(self.cfg)
self.epa_alignment = m.merge(refaln, self.tmpquery)
else:
self.cfg.log.info("Query sequences are not aligned")
self.cfg.log.info(
"Align query sequences to the reference alignment using HMMER")
self.require_hmmer()
self.align_to_refenence(self.noalign, minp=minp)
def print_ranks(self, rks, confs, minlw=0.0):
uncorr_ranks = self.refjson.get_uncorr_ranks(rks)
ss = ""
css = ""
for i in range(len(uncorr_ranks)):
conf = confs[i]
if conf == confs[0] and confs[0] >= 0.99:
conf = 1.0
if conf >= minlw:
ss = ss + uncorr_ranks[i] + ";"
css = css + "{0:.3f}".format(conf) + ";"
else:
break
if ss == "":
return None
else:
return ss[:-1] + "\t" + css[:-1]
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 run_ptp(self, jp):
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()
def print_result_line(self, fo, line):
if self.cfg.verbose:
print(line)
if fo:
fo.write(line + "\n")
def get_noalign_list(self):
noalign_list = []
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)
noalign_list.append(origin_taxon_name)
return noalign_list
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)
jp = self.run_epa()
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"]
ranks, lws = self.classify_helper.classify_seq(edges)
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 self.cfg.check_novelty:
isnovo = self.novelty_check(place_edge=str(edges[0][0]),
ranks=ranks,
lws=lws)
output += "*" if isnovo else "o"
self.print_result_line(fo, output)
noassign_list += self.get_noalign_list()
for taxon_name in noassign_list:
output = "%s\t\t\t?" % origin_taxon_name
self.print_result_line(fo, output)
if fo:
fo.close()
#############################################
#
# EPA-PTP species delimitation
#
#############################################
if ptp:
self.run_ptp(jp)
def novelty_check(self, place_edge, ranks, lws):
"""If the taxonomic assignment is not assigned to the genus level,
we need to check if it is due to the incomplete reference taxonomy or
it is likely to be something new:
1. If the final ranks are assinged because of lw cut, that means with samller lw
the ranks can be further assinged to lowers. This indicate the undetermined ranks
in the assignment is not due to the incomplete reference taxonomy, so the query
sequence is likely to be something new.
2. Otherwise We check all leaf nodes' immediate lower rank below this ml placement point,
if they are not empty, output all ranks and indicate this could be novelty.
"""
lowrank = 0
for i in max(range(len(ranks)), 6):
"""above genus level"""
rk = ranks[i]
lw = lws[i]
if rk == "-":
break
else:
lowrank = lowrank + 1
if lw >= 0 and lw < self.cfg.min_lhw:
return True
if lowrank >= 5 and lowrank < len(ranks) and not ranks[lowrank] == "-":
return False
else:
placenode = self.reftree.search_nodes(B=place_edge)[0]
if placenode.is_leaf():
return False
else:
leafnodes = placenode.get_leaves()
flag = True
for leaf in leafnodes:
br_num = leaf.B
branks = self.bid_taxonomy_map[br_num]
if lowrank >= len(branks) or branks[lowrank] == "-":
flag = False
break
return flag
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))
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 EpaClassifier:
def __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
print("Invalid json file format!")
sys.exit()
#validate input json format
self.refjson.validate()
self.bid_taxonomy_map = self.refjson.get_bid_tanomomy_map()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
self.cfg.compress_patterns = self.refjson.get_pattern_compression()
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, args.p_value, self.rate, self.node_height)
def cleanup(self):
FileUtils.remove_if_exists(self.tmp_refaln)
FileUtils.remove_if_exists(self.epa_alignment)
FileUtils.remove_if_exists(self.hmmprofile)
FileUtils.remove_if_exists(self.tmpquery)
FileUtils.remove_if_exists(self.noalign)
def align_to_refenence(self, noalign, minp = 0.9):
refaln = self.refjson.get_alignment(fout = self.tmp_refaln)
fprofile = self.refjson.get_hmm_profile(self.hmmprofile)
# if there is no hmmer profile in json file, build it from scratch
if not fprofile:
hmm = hmmer(self.cfg, refaln)
fprofile = hmm.build_hmm_profile()
hm = hmmer(config = self.cfg, refalign = refaln , query = self.tmpquery, refprofile = fprofile, discard = noalign, seqs = self.seqs, minp = minp)
self.epa_alignment = hm.align()
def merge_alignment(self, query_seqs):
refaln = self.refjson.get_alignment_list()
with open(self.epa_alignment, "w") as fout:
for seq in refaln:
fout.write(">" + seq[0] + "\n" + seq[1] + "\n")
for name, seq, comment, sid in query_seqs.iter_entries():
fout.write(">" + name + "\n" + seq + "\n")
def checkinput(self, query_fname, minp = 0.9):
formats = ["fasta", "phylip", "iphylip", "phylip_relaxed", "iphylip_relaxed"]
for fmt in formats:
try:
self.seqs = SeqGroup(sequences=query_fname, format = fmt)
break
except:
print("Guessing input format: not " + fmt)
if self.seqs == None:
print("Invalid input file format!")
print("The supported input formats are fasta and phylip")
sys.exit()
if self.ignore_refalign:
print("Assuming query file contains reference sequences, skipping the alignment step...")
with open(self.epa_alignment, "w") as fout:
for name, seq, comment, sid in self.seqs.iter_entries():
ref_name = self.REF_PREFIX + name
if ref_name in self.refjson.get_sequences_names():
seq_name = ref_name
else:
seq_name = EpacConfig.QUERY_SEQ_PREFIX + name
fout.write(">" + seq_name + "\n" + seq + "\n")
return
# add query seq name prefix to avoid confusion between reference and query sequences
self.seqs.add_name_prefix(EpacConfig.QUERY_SEQ_PREFIX)
self.seqs.write(format="fasta", outfile=self.tmpquery)
print("Checking if query sequences are aligned ...")
entries = self.seqs.get_entries()
seql = len(entries[0][1])
aligned = True
for entri in entries[1:]:
l = len(entri[1])
if not seql == l:
aligned = False
break
if aligned and len(self.seqs) > 1:
print("Query sequences are aligned")
refalnl = self.refjson.get_alignment_length()
if refalnl == seql:
print("Merging query alignment with reference alignment")
self.merge_alignment(self.seqs)
else:
print("Merging query alignment with reference alignment using MUSCLE")
require_muscle()
refaln = self.refjson.get_alignment(fout = self.tmp_refaln)
m = muscle(self.cfg)
self.epa_alignment = m.merge(refaln, self.tmpquery)
else:
print("Query sequences are not aligned")
print("Align query sequences to the reference alignment using HMMER")
require_hmmer()
self.align_to_refenence(self.noalign, minp = minp)
print("Running EPA ......")
print("")
def print_ranks(self, rks, confs, minlw = 0.0):
ss = ""
css = ""
for i in range(len(rks)):
conf = confs[i]
if conf == confs[0] and confs[0] >=0.99:
conf = 1.0
if conf >= minlw:
ss = ss + rks[i] + ";"
css = css + "{0:.3f}".format(conf) + ";"
else:
break
if ss == "":
return None
else:
return ss[:-1] + "\t" + css[:-1]
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 novelty_check(self, place_edge, ranks, lws, minlw):
"""If the taxonomic assignment is not assigned to the genus level,
we need to check if it is due to the incomplete reference taxonomy or
it is likely to be something new:
1. If the final ranks are assinged because of lw cut, that means with samller lw
the ranks can be further assinged to lowers. This indicate the undetermined ranks
in the assignment is not due to the incomplete reference taxonomy, so the query
sequence is likely to be something new.
2. Otherwise We check all leaf nodes' immediate lower rank below this ml placement point,
if they are not empty, output all ranks and indicate this could be novelty.
"""
lowrank = 0
for i in range(len(ranks)):
if i < 6:
"""above genus level"""
rk = ranks[i]
lw = lws[i]
if rk == "-":
break
else:
lowrank = lowrank + 1
if lw >=0 and lw < minlw:
return True
if lowrank >= 5 and not ranks[lowrank] == "-":
return False
else:
placenode = self.reftree.search_nodes(B = place_edge)[0]
if placenode.is_leaf():
return False
else:
leafnodes = placenode.get_leaves()
flag = True
for leaf in leafnodes:
br_num = leaf.B
branks = self.bid_taxonomy_map[br_num]
if branks[lowrank] == "-":
flag = False
break
return flag
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))
class EpaClassifier:
def __init__(self, config, args):
self.cfg = config
self.jplace_fname = args.jplace_fname
self.ignore_refalign = args.ignore_refalign
self.tmp_refaln = config.tmp_fname("%NAME%.refaln")
#here is the final alignment file for running EPA
self.epa_alignment = config.tmp_fname("%NAME%.afa")
self.hmmprofile = config.tmp_fname("%NAME%.hmmprofile")
self.tmpquery = config.tmp_fname("%NAME%.tmpquery")
self.noalign = config.tmp_fname("%NAME%.noalign")
self.seqs = None
assign_fname = args.output_name + ".assignment.txt"
self.out_assign_fname = os.path.join(args.output_dir, assign_fname)
jplace_fname = args.output_name + ".jplace"
self.out_jplace_fname = os.path.join(args.output_dir, jplace_fname)
try:
self.refjson = RefJsonParser(config.refjson_fname)
except ValueError:
self.cfg.exit_user_error("Invalid json file format: %s" % config.refjson_fname)
#validate input json format
self.refjson.validate()
self.reftree = self.refjson.get_reftree()
self.rate = self.refjson.get_rate()
self.node_height = self.refjson.get_node_height()
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.refjson.get_origin_taxonomy())
th.set_mf_rooted_tree(self.refjson.get_tax_tree())
th.set_bf_unrooted_tree(self.refjson.get_reftree())
self.bid_taxonomy_map = th.get_bid_taxonomy_map()
self.cfg.log.info("Loaded reference tree with %d taxa\n" % len(self.reftree.get_leaves()))
self.classify_helper = TaxClassifyHelper(self.cfg, self.bid_taxonomy_map, self.rate, self.node_height)
def require_muscle(self):
basepath = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists(basepath + "/epac/bin/muscle"):
errmsg = "The pipeline uses MUSCLE to merge alignments, please download the programm from:\n" + \
"http://www.drive5.com/muscle/downloads.htm\n" + \
"and specify path to your installation in the config file (sativa.cfg)\n"
self.cfg.exit_user_error(errmsg)
def require_hmmer(self):
basepath = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists(basepath + "/epac/bin/hmmbuild") or not os.path.exists(basepath + "/epac/bin/hmmalign"):
errmsg = "The pipeline uses HAMMER to align the query seqeunces, please download the programm from:\n" + \
"http://hmmer.janelia.org/\n" + \
"and specify path to your installation in the config file (sativa.cfg)\n"
self.cfg.exit_user_error(errmsg)
def align_to_refenence(self, noalign, minp = 0.9):
refaln = self.refjson.get_alignment(fout = self.tmp_refaln)
fprofile = self.refjson.get_hmm_profile(self.hmmprofile)
# if there is no hmmer profile in json file, build it from scratch
if not fprofile:
hmm = hmmer(self.cfg, refaln)
fprofile = hmm.build_hmm_profile()
hm = hmmer(config = self.cfg, refalign = refaln , query = self.tmpquery, refprofile = fprofile, discard = noalign, seqs = self.seqs, minp = minp)
self.epa_alignment = hm.align()
def merge_alignment(self, query_seqs):
refaln = self.refjson.get_alignment_list()
with open(self.epa_alignment, "w") as fout:
for seq in refaln:
fout.write(">" + seq[0] + "\n" + seq[1] + "\n")
for name, seq, comment, sid in query_seqs.iter_entries():
fout.write(">" + name + "\n" + seq + "\n")
def checkinput(self, query_fname, minp = 0.9):
formats = ["fasta", "phylip", "iphylip", "phylip_relaxed", "iphylip_relaxed"]
for fmt in formats:
try:
self.seqs = SeqGroup(sequences=query_fname, format = fmt)
break
except:
self.cfg.log.debug("Guessing input format: not " + fmt)
if self.seqs == None:
self.cfg.exit_user_error("Invalid input file format: %s\nThe supported input formats are fasta and phylip" % query_fname)
if self.ignore_refalign:
self.cfg.log.info("Assuming query file contains reference sequences, skipping the alignment step...\n")
self.query_count = 0
with open(self.epa_alignment, "w") as fout:
for name, seq, comment, sid in self.seqs.iter_entries():
ref_name = self.refjson.get_corr_seqid(EpacConfig.REF_SEQ_PREFIX + name)
if ref_name in self.refjson.get_sequences_names():
seq_name = ref_name
else:
seq_name = EpacConfig.QUERY_SEQ_PREFIX + name
self.query_count += 1
fout.write(">" + seq_name + "\n" + seq + "\n")
return
self.query_count = len(self.seqs)
# add query seq name prefix to avoid confusion between reference and query sequences
self.seqs.add_name_prefix(EpacConfig.QUERY_SEQ_PREFIX)
self.seqs.write(format="fasta", outfile=self.tmpquery)
self.cfg.log.info("Checking if query sequences are aligned ...")
entries = self.seqs.get_entries()
seql = len(entries[0][1])
aligned = True
for entri in entries[1:]:
l = len(entri[1])
if not seql == l:
aligned = False
break
if aligned and len(self.seqs) > 1:
self.cfg.log.info("Query sequences are aligned")
refalnl = self.refjson.get_alignment_length()
if refalnl == seql:
self.cfg.log.info("Merging query alignment with reference alignment")
self.merge_alignment(self.seqs)
else:
self.cfg.log.info("Merging query alignment with reference alignment using MUSCLE")
self.require_muscle()
refaln = self.refjson.get_alignment(fout = self.tmp_refaln)
m = muscle(self.cfg)
self.epa_alignment = m.merge(refaln, self.tmpquery)
else:
self.cfg.log.info("Query sequences are not aligned")
self.cfg.log.info("Align query sequences to the reference alignment using HMMER")
self.require_hmmer()
self.align_to_refenence(self.noalign, minp = minp)
def print_ranks(self, rks, confs, minlw = 0.0):
uncorr_ranks = self.refjson.get_uncorr_ranks(rks)
ss = ""
css = ""
for i in range(len(uncorr_ranks)):
conf = confs[i]
if conf == confs[0] and confs[0] >=0.99:
conf = 1.0
if conf >= minlw:
ss = ss + uncorr_ranks[i] + ";"
css = css + "{0:.3f}".format(conf) + ";"
else:
break
if ss == "":
return None
else:
return ss[:-1] + "\t" + css[:-1]
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()
#############################################
def novelty_check(self, place_edge, ranks, lws):
"""If the taxonomic assignment is not assigned to the genus level,
we need to check if it is due to the incomplete reference taxonomy or
it is likely to be something new:
1. If the final ranks are assinged because of lw cut, that means with samller lw
the ranks can be further assinged to lowers. This indicate the undetermined ranks
in the assignment is not due to the incomplete reference taxonomy, so the query
sequence is likely to be something new.
2. Otherwise We check all leaf nodes' immediate lower rank below this ml placement point,
if they are not empty, output all ranks and indicate this could be novelty.
"""
lowrank = 0
for i in range(len(ranks)):
if i < 6:
"""above genus level"""
rk = ranks[i]
lw = lws[i]
if rk == "-":
break
else:
lowrank = lowrank + 1
if lw >=0 and lw < self.cfg.min_lhw:
return True
if lowrank >= 5 and lowrank < len(ranks) and not ranks[lowrank] == "-":
return False
else:
placenode = self.reftree.search_nodes(B = place_edge)[0]
if placenode.is_leaf():
return False
else:
leafnodes = placenode.get_leaves()
flag = True
for leaf in leafnodes:
br_num = leaf.B
branks = self.bid_taxonomy_map[br_num]
if branks[lowrank] == "-":
flag = False
break
return flag
