def convert(self, infile, count=False):
from ete3 import NCBITaxa
tax_db = NCBITaxa()
with open(infile) as handle:
header = None
counter = 0
while not header and counter < 15:
head = handle.readline().rstrip().split("\t")
if head[0] == "#query_name":
header = head
counter += 1
if not header:
print(
"\nYou sure this file is good? Like, is it the '.emapper.annotations' you got from running eggnoggmapper?\n"
)
sys.exit(0)
idx = [i for i, v in enumerate(header) if v == "eggNOG OGs"][0]
print("Loading eggNOGs from file.", file=sys.stderr)
gene_id2eggs = {
l.split("\t")[0]: l.split("\t")[idx]
for l in tqdm(handle.readlines()) if not l.startswith("#")
}
print("Parsing taxonomies, and simplifying to deepest eggNOG.",
file=sys.stderr)
taxos = {
vv.split("@")[1]
for v in gene_id2eggs.values() for vv in v.split(",")
}
tax2level = {
k: len(v)
for k, v in tax_db.get_lineage_translator(list(taxos)).items()
}
lowest_level = lambda x: tax2level.get(int(x[1]), 1000)
gene_id2deepest_egg = {
k: min([vv.split('@') for vv in v.split(",")], key=lowest_level)[0]
for k, v in tqdm(gene_id2eggs.items())
}
print("Stratify it to genome", file=sys.stderr)
if not self.gene_id2genome:
self.gene_id2genome = {
k: "_".join(k.split("_")[:-1])
for k in gene_id2deepest_egg.keys()
}
genome2nog = {k: [] for k in set(self.gene_id2genome.values())}
for k, v in gene_id2deepest_egg.items():
genome2nog[self.gene_id2genome[k]] += [v]
if count:
return {
k: {vv: v.count(vv)
for vv in set(v)}
for k, v in genome2nog.items()
}
else:
return {k: list(set(v)) for k, v in genome2nog.items()}
def findclade(namelist, ranks='family|genus'):
#rankregex = re.compile('^(%s)$' % ranks)
ncbi = NCBITaxa()
name2taxid = ncbi.get_name_translator(namelist)
lineages = ncbi.get_lineage_translator([v[0] for v in name2taxid.values()])
cladetaxids = []
for name in namelist:
lineage = lineages[name2taxid[name][0]]
#print(name, name2taxid[name], lineage)
rank2clade = {
rk: taxid
for taxid, rk in ncbi.get_rank(lineage).items()
}
cladetaxids.append(
[rank2clade.get(rank, 0) for rank in ranks.split('|')])
#print(cladetaxids)
taxid2clade = ncbi.get_taxid_translator(chain(*cladetaxids))
for name, taxidlist in zip(namelist, cladetaxids):
yield name, [taxid2clade.get(t, '') for t in taxidlist]
emapper_outs = []
for v in os.walk(os.path.dirname(genus), followlinks=True):
for vv in v[2]:
if vv.endswith(".emapper") and not "__" in vv:
emapper_outs += [pjoin(v[0], vv)]
for f in emapper_outs:
with open(f) as handle:
res = json.load(handle)
gid = os.path.basename(f)[:-8]
for k,v in res.items():
if v :
nogs = v["eggNOG OGs"]
if nogs not in nogs2lowest:
taxos = [v.split("@")[1] for v in nogs.split(",")]
tax2level = {k : len(v) for k,v in tax_db.get_lineage_translator(list(taxos)).items()}
lowest_nog = min([vv.split('@') for vv in nogs.split(",")], key = lambda x : tax2level.get(int(x[1]),1000))[0]
nogs2lowest[nogs] = lowest_nog
else :
lowest_nog = nogs2lowest[nogs]
nog2ko[lowest_nog] += [v['KEGG_ko']]
nog2cogcat[lowest_nog] += [v['COG Functional cat.']]
nog2ec[lowest_nog] += [v['EC']]
nog2cazy[lowest_nog] += [v['CAZy']]
if k in gid2abinit:
abinit2ko[gid2abinit[k]] += [v['KEGG_ko']]
abinit2cogcat[gid2abinit[k]] += [v['COG Functional cat.']]
abinit2ec[gid2abinit[k]] += [v['EC']]
abinit2cazy[gid2abinit[k]] += [v['CAZy']]
gid2eggs = {}
for g in tqdm(gids):
patty = pjoin(clade_folder, g, g + ".emapper")
with open(patty) as handle:
gid2eggs[g] = {
v["eggNOG OGs"]
for k, v in json.load(handle).items() if v
}
taxos = {
vvv.split("@")[1]
for v in gid2eggs.values() for vv in v for vvv in vv.split(",")
}
tax2level = {
k: len(v)
for k, v in tax_db.get_lineage_translator(list(taxos)).items()
}
for g in tqdm(gids):
gid2eggs[g] = list({
min([vv.split('@') for vv in v.split(",")],
key=lambda x: tax2level.get(int(x[1]), 1000))[0]
for v in gid2eggs[g]
})
#checking checkm file
completeness_switch = "--checkm " + checkm_file if os.path.exists(
checkm_file) else ""
print("executing mOTUpan")
with tempfile.NamedTemporaryFile(mode="w", suffix=".gid2cog") as temp:
json.dump(gid2eggs, temp, indent=4, sort_keys=True)
def create_CAMI_profile(data_file, sample_id):
"""
CSV Parser for converting information to the CAMI profiling
format.
Input: csv file with the required information, sample ID
and the name of the file to write to
Output: header and contents of the CAMI profile file
(see format linked above)
"""
dataframe = pd.read_csv(data_file)
subset = dataframe[dataframe["sample"] == sample_id]
taxa = subset["Assignment"]
total_percentages = subset["percentage_of_total_reads"]
ncbi = NCBITaxa()
rank_list_list = [] #save all taxonomies to find the longest
#I use the longest, because virus taxonomy is diverse...
output_list = [] #stores the CAMI profiles as strings
for name in taxa:
#remove names that have some addition in brackets,
# like " (segment 1)"
if ' (' in name:
ncbi_name = name[:name.index(' (')]
else:
ncbi_name = name
taxon_and_id = ncbi.get_name_translator([ncbi_name])
#ncbi.get_name_translator() returns a dictionary { 'taxon' : [id]}
taxid = taxon_and_id[ncbi_name]
#taxid is a list with one number
taxid_nr = taxid[0]
rank_dict = ncbi.get_rank(taxid)
#ncbi.get_rank() requires a list of IDs, and returns a dictionary:
# {id: 'rank'}
rank = rank_dict[taxid_nr]
tax_path_dict = ncbi.get_lineage_translator(taxid) #[taxid_nr]
#ncbi.get_lineage_translator() requires a list of IDs, and returns
# a dictionary {leaf_id: [root_id, node_id, leaf_id]}
tax_path = tax_path_dict[taxid_nr][1:]
tax_path_sn = []
#with a for-loop you can translate the taxids in the list
# 'tax_path' to their corresponding scientific names (sn)
for t in tax_path:
tax_path_sn.append(ncbi.get_taxid_translator([t])[t])
rank_list = []
#Making this list requires using a for-loop;
# using the function on a list makes an UNORDERED dictionary
#Also, since the path differs between branches, I will look
# for the longest using a list of lists
for taxid in tax_path:
rank_dict = ncbi.get_rank([taxid])
rank = rank_dict[taxid]
rank_list.append(rank)
rank_list_list.append(rank_list)
tax_path_string = '|'.join(map(str, tax_path))
tax_path_sn_string = '|'.join(tax_path_sn)
percentage = subset.loc[subset["Assignment"] ==
name]["percentage_of_total_reads"].values[0]
output_line = "%s\t%s\t%s\t%s\t%s" % (taxid_nr, rank, tax_path_string,
tax_path_sn_string, percentage)
output_list.append(output_line)
longest_taxonomy = '|'.join(max(rank_list_list, key=len))
#Read the specification for details about this header:
#https://github.com/bioboxes/rfc/blob/60263f34c57bc4137deeceec4c68a7f9f810f6a5/data-format/profiling.mkd
header = """# Taxonomic Profiling Output
@SampleID:%s
@Version:0.9.3
@Ranks:%s\t#the longest path in this sample: virus taxonomy is messy
@TaxonomyID:ncbi-taxonomy_2018-05-25
@@TAXID\tRANK\tTAXPATH\tTAXPATHSN\tPERCENTAGE
""" % (sample_id, longest_taxonomy)
return (header, output_list)
mag2md[k]['sample derived from'] = ass_md['sample_accession']
mag2md[k]['ENA-CHECKLIST'] = 'ERC000047'
mag2md[k]['isolation_source'] = " ".join(ass_md['taxonomy'].split()[:-1])
mag2md[k]['metagenomic source'] = ass_md['ncbi_taxid']
del mag2md[k]['ncbi_taxid']
del mag2md[k]['taxonomy']
if 'sample_accession' in mag2md[k]:
del mag2md[k]['sample_accession']
if 'Run' in mag2md[k]:
del mag2md[k]['Run']
if 'Lake_code' in mag2md[k]:
del mag2md[k]['Lake_code']
get_parent = lambda taxid: max({
k: v
for k, v in ncbi.get_lineage_translator(ncbi.get_lineage(taxid)).items()
if k != taxid
}.items(),
key=lambda l: len(l[1]))[0]
tax2uncul = {}
for k, v in set(found_taxo.values()):
options = ncbi.get_name_translator(
["uncultured " + k + " bacterium", "uncultured " + k + " archaeon"])
if k == 'root':
tax2uncul[k] = ('uncultured prokaryote', 198431)
continue
if not options:
options = ncbi.get_name_translator(["uncultured " + k + " sp."])
if not options:
options = ncbi.get_name_translator(
["uncultured " + k + " cyanobacterium"])
