def nonrep_radius(self, unclustered_gids, rep_gids, ani_af_rep_vs_nonrep):
"""Calculate circumscription radius for unclustered, nontype genomes."""
# set radius for genomes to default values
nonrep_radius = {}
for gid in unclustered_gids:
nonrep_radius[gid] = GenomeRadius(ani=self.ani_sp,
af=None,
neighbour_gid=None)
# determine closest type ANI neighbour and restrict ANI radius as necessary
ani_af = pickle.load(open(ani_af_rep_vs_nonrep, 'rb'))
for nonrep_gid in unclustered_gids:
if nonrep_gid not in ani_af:
continue
for rep_gid in rep_gids:
if rep_gid not in ani_af[nonrep_gid]:
continue
ani, af = FastANI.symmetric_ani(ani_af, nonrep_gid, rep_gid)
if ani > nonrep_radius[nonrep_gid].ani and af >= self.af_sp:
nonrep_radius[nonrep_gid] = GenomeRadius(ani=ani,
af=af,
neighbour_gid=rep_gid)
self.logger.info('ANI circumscription radius: min={:.2f}, mean={:.2f}, max={:.2f}'.format(
min([d.ani for d in nonrep_radius.values()]),
np_mean([d.ani for d in nonrep_radius.values()]),
max([d.ani for d in nonrep_radius.values()])))
return nonrep_radius
def _cluster(self, ani_af, non_reps, rep_radius):
"""Cluster non-representative to representative genomes using species specific ANI thresholds."""
clusters = {}
for rep_id in rep_radius:
clusters[rep_id] = []
num_clustered = 0
for idx, non_rid in enumerate(non_reps):
if idx % 100 == 0:
sys.stdout.write('==> Processed {:,} of {:,} genomes [no. clustered = {:,}].\r'.format(
idx+1,
len(non_reps),
num_clustered))
sys.stdout.flush()
if non_rid not in ani_af:
continue
closest_rid = None
closest_ani = 0
closest_af = 0
for rid in rep_radius:
if rid not in ani_af[non_rid]:
continue
ani, af = FastANI.symmetric_ani(ani_af, rid, non_rid)
if af >= self.af_sp:
if ani > closest_ani or (ani == closest_ani and af > closest_af):
closest_rid = rid
closest_ani = ani
closest_af = af
if closest_rid:
if closest_ani > rep_radius[closest_rid].ani:
num_clustered += 1
clusters[closest_rid].append(self.ClusteredGenome(gid=non_rid,
ani=closest_ani,
af=closest_af))
sys.stdout.write('==> Processed {:,} of {:,} genomes [no. clustered = {:,}].\r'.format(
len(non_reps),
len(non_reps),
num_clustered))
sys.stdout.flush()
sys.stdout.write('\n')
num_unclustered = len(non_reps) - num_clustered
self.logger.info('Assigned {:,} genomes to {:,} representatives; {:,} genomes remain unclustered.'.format(
sum([len(clusters[rid]) for rid in clusters]),
len(clusters),
num_unclustered))
return clusters
def run(self, target_genus, gtdb_metadata_file, genomic_path_file):
"""Dereplicate GTDB species clusters using ANI/AF criteria."""
# create GTDB genome sets
self.logger.info('Creating GTDB genome set.')
genomes = Genomes()
genomes.load_from_metadata_file(gtdb_metadata_file)
genomes.load_genomic_file_paths(genomic_path_file)
self.logger.info(
' - genome set has {:,} species clusters spanning {:,} genomes.'.
format(len(genomes.sp_clusters),
genomes.sp_clusters.total_num_genomes()))
# identify GTDB representatives from target genus
self.logger.info('Identifying GTDB representatives from target genus.')
target_gids = set()
for gid in genomes:
if genomes[gid].is_gtdb_sp_rep(
) and genomes[gid].gtdb_taxa.genus == target_genus:
target_gids.add(gid)
self.logger.info(' - identified {:,} genomes.'.format(
len(target_gids)))
# calculate FastANI ANI/AF between target genomes
self.logger.info('Calculating pairwise ANI between target genomes.')
ani_af = self.fastani.pairwise(target_gids,
genomes.genomic_files,
check_cache=True)
self.fastani.write_cache(silence=True)
# write out results
genus_label = target_genus.replace('g__', '').lower()
fout = open(
os.path.join(self.output_dir,
'{}_rep_ani.tsv'.format(genus_label)), 'w')
fout.write(
'Query ID\tQuery species\tTarget ID\tTarget species\tANI\tAF\n')
for qid in target_gids:
for rid in target_gids:
ani, af = FastANI.symmetric_ani(ani_af, qid, rid)
fout.write('{}\t{}\t{}\t{}\t{:.3f}\t{:.3f}\n'.format(
qid, genomes[qid].gtdb_taxa.species, rid,
genomes[rid].gtdb_taxa.species, ani, af))
fout.close()
def rep_genome_stats(self, clusters, genome_files):
"""Calculate statistics relative to representative genome."""
self.logger.info('Calculating statistics to cluster representatives:')
stats = {}
for idx, (rid, cids) in enumerate(clusters.items()):
if len(cids) == 0:
stats[rid] = self.RepStats(min_ani=-1,
mean_ani=-1,
std_ani=-1,
median_ani=-1)
else:
# calculate ANI to representative genome
gid_pairs = []
for cid in cids:
gid_pairs.append((cid, rid))
gid_pairs.append((rid, cid))
if True: # *** DEBUGGING
ani_af = self.fastani.pairs(gid_pairs,
genome_files,
report_progress=False)
else:
ani_af = self.fastani.ani_cache
# calculate statistics
anis = [FastANI.symmetric_ani(ani_af, cid, rid)[
0] for cid in cids]
stats[rid] = self.RepStats(min_ani=min(anis),
mean_ani=np_mean(anis),
std_ani=np_std(anis),
median_ani=np_median(anis))
statusStr = '-> Processing %d of %d (%.2f%%) clusters.'.ljust(86) % (
idx+1,
len(clusters),
float((idx+1)*100)/len(clusters))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
sys.stdout.write('\n')
return stats
def find_multiple_reps(self, clusters, cluster_radius):
"""Determine number of non-rep genomes within ANI radius of multiple rep genomes.
This method assumes the ANI cache contains all relevant ANI calculations between
representative and non-representative genomes. This is the case once the de novo
clustering has been performed.
"""
self.logger.info(
'Determine number of non-rep genomes within ANI radius of multiple rep genomes.')
# get clustered genomes IDs
clustered_gids = []
for rid in clusters:
clustered_gids += clusters[rid]
self.logger.info('Considering {:,} representatives and {:,} non-representative genomes.'.format(
len(clusters),
len(clustered_gids)))
nonrep_rep_count = defaultdict(set)
for idx, gid in enumerate(clustered_gids):
cur_ani_cache = self.fastani.ani_cache[gid]
for rid in clusters:
if rid not in cur_ani_cache:
continue
ani, af = FastANI.symmetric_ani(
self.fastani.ani_cache, gid, rid)
if af >= self.af_sp and ani >= cluster_radius[rid].ani:
nonrep_rep_count[gid].add((rid, ani))
if (idx+1) % 100 == 0 or (idx+1) == len(clustered_gids):
statusStr = '-> Processing %d of %d (%.2f%%) clusters genomes.'.ljust(86) % (
idx+1,
len(clustered_gids),
float((idx+1)*100)/len(clustered_gids))
sys.stdout.write('%s\r' % statusStr)
sys.stdout.flush()
sys.stdout.write('\n')
return nonrep_rep_count
def top_hits(self, species, rid, ani_af, genomes):
"""Report top 5 hits to species."""
results = {}
for qid in ani_af[rid]:
ani, af = FastANI.symmetric_ani(ani_af, rid, qid)
results[qid] = (ani, af)
self.logger.info(f'Closest 5 species to {species} ({rid}):')
idx = 0
for qid, (ani, af) in sorted(results.items(),
key=lambda x: x[1],
reverse=True):
q_species = genomes[qid].gtdb_species
self.logger.info(
f'{q_species} ({qid}): ANI={ani:.1f}%, AF={af:.2f}')
if idx == 5:
break
idx += 1
def merge_ani_radius(self, species, rid, merged_sp_cluster, genomic_files):
"""Determine ANI radius if species were merged."""
self.logger.info(
f'Calculating ANI from {species} to all genomes in merged species cluster.'
)
gid_pairs = []
for gid in merged_sp_cluster:
gid_pairs.append((rid, gid))
gid_pairs.append((gid, rid))
merged_ani_af1 = self.fastani.pairs(gid_pairs, genomic_files)
ani_radius = 100
for gid in merged_sp_cluster:
ani, af = FastANI.symmetric_ani(merged_ani_af1, rid, gid)
if ani < ani_radius:
ani_radius = ani
af_radius = af
self.logger.info(
f'Merged cluster with {species} rep: ANI radius={ani_radius:.1f}%, AF={af_radius:.2f}'
)
def calculate_type_strain_ani(self, ncbi_sp, type_gids, cur_genomes,
use_pickled_results):
"""Calculate pairwise ANI between type strain genomes."""
ncbi_sp_str = ncbi_sp[3:].lower().replace(' ', '_')
if not use_pickled_results: # ***
ani_af = self.fastani.pairwise(type_gids,
cur_genomes.genomic_files)
pickle.dump(
ani_af,
open(os.path.join(self.ani_pickle_dir, f'{ncbi_sp_str}.pkl'),
'wb'))
else:
ani_af = pickle.load(
open(os.path.join(self.ani_pickle_dir, f'{ncbi_sp_str}.pkl'),
'rb'))
anis = []
afs = []
gid_anis = defaultdict(lambda: {})
gid_afs = defaultdict(lambda: {})
all_similar = True
for gid1, gid2 in combinations(type_gids, 2):
ani, af = FastANI.symmetric_ani(ani_af, gid1, gid2)
if ani < 99 or af < 0.65:
all_similar = False
anis.append(ani)
afs.append(af)
gid_anis[gid1][gid2] = ani
gid_anis[gid2][gid1] = ani
gid_afs[gid1][gid2] = af
gid_afs[gid2][gid1] = af
return all_similar, anis, afs, gid_anis, gid_afs
def run(self, gtdb_metadata_file, genome_path_file, species1, species2):
"""Produce information relevant to merging two sister species."""
# read GTDB species clusters
self.logger.info('Reading GTDB species clusters.')
genomes = Genomes()
genomes.load_from_metadata_file(gtdb_metadata_file)
genomes.load_genomic_file_paths(genome_path_file)
self.logger.info(
' - identified {:,} species clusters spanning {:,} genomes.'.
format(len(genomes.sp_clusters),
genomes.sp_clusters.total_num_genomes()))
# find species of interest
gid1 = None
gid2 = None
for gid, species in genomes.sp_clusters.species():
if species == species1:
gid1 = gid
elif species == species2:
gid2 = gid
if gid1 is None:
self.logger.error(
f'Unable to find representative genome for {species1}.')
sys.exit(-1)
if gid2 is None:
self.logger.error(
f'Unable to find representative genome for {species2}.')
sys.exit(-1)
self.logger.info(' - identified {:,} genomes in {}.'.format(
len(genomes.sp_clusters[gid1]), species1))
self.logger.info(' - identified {:,} genomes in {}.'.format(
len(genomes.sp_clusters[gid2]), species2))
# calculate ANI between all genome in genus
genus1 = genomes[gid1].gtdb_genus
genus2 = genomes[gid2].gtdb_genus
if genus1 != genus2:
self.logger.error(
f'Genomes must be from same genus: {genus1} {genus2}')
sys.exit(-1)
self.logger.info(f'Identifying {genus1} species representatives.')
reps_in_genera = set()
for rid in genomes.sp_clusters:
if genomes[rid].gtdb_genus == genus1:
reps_in_genera.add(rid)
self.logger.info(
f' - identified {len(reps_in_genera):,} representatives.')
# calculate ANI between genomes
self.logger.info(f'Calculating ANI to {species1}.')
gid_pairs = []
for gid in reps_in_genera:
if gid != gid1:
gid_pairs.append((gid1, gid))
gid_pairs.append((gid, gid1))
ani_af1 = self.fastani.pairs(gid_pairs, genomes.genomic_files)
self.logger.info(f'Calculating ANI to {species2}.')
gid_pairs = []
for gid in reps_in_genera:
if gid != gid2:
gid_pairs.append((gid2, gid))
gid_pairs.append((gid, gid2))
ani_af2 = self.fastani.pairs(gid_pairs, genomes.genomic_files)
# report results
ani12, af12 = ani_af1[gid1][gid2]
ani21, af21 = ani_af2[gid2][gid1]
ani, af = FastANI.symmetric_ani(ani_af1, gid1, gid2)
self.logger.info(
f'{species1} ({gid1}) -> {species2} ({gid2}): ANI={ani12:.1f}%, AF={af12:.2f}'
)
self.logger.info(
f'{species2} ({gid2}) -> {species1} ({gid1}): ANI={ani21:.1f}%, AF={af21:.2f}'
)
self.logger.info(f'Max. ANI={ani:.1f}%, Max. AF={af:.2f}')
# report top hits
self.top_hits(species1, gid1, ani_af1, genomes)
self.top_hits(species2, gid2, ani_af2, genomes)
# calculate ANI from species to all genomes in merged species cluster
merged_sp_cluster = genomes.sp_clusters[gid1].union(
genomes.sp_clusters[gid2])
self.merge_ani_radius(species1, gid1, merged_sp_cluster,
genomes.genomic_files)
self.merge_ani_radius(species2, gid2, merged_sp_cluster,
genomes.genomic_files)
def pairwise_stats(self, clusters, genome_files):
"""Calculate statistics for all pairwise comparisons in a species cluster."""
self.logger.info(
f'Restricting pairwise comparisons to {self.max_genomes_for_stats:,} randomly selected genomes.')
self.logger.info(
'Calculating statistics for all pairwise comparisons in a species cluster:')
stats = {}
for idx, (rid, cids) in enumerate(clusters.items()):
statusStr = '-> Processing {:,} of {:,} ({:2f}%) clusters (size = {:,}).'.ljust(86).format(
idx+1,
len(clusters),
float((idx+1)*100)/len(clusters),
len(cids))
sys.stdout.write('{}\r'.format(statusStr))
sys.stdout.flush()
if len(cids) == 0:
stats[rid] = self.PairwiseStats(min_ani=-1,
mean_ani=-1,
std_ani=-1,
median_ani=-1,
ani_to_medoid=-1,
mean_ani_to_medoid=-1,
mean_ani_to_rep=-1,
ani_below_95=-1)
else:
if len(cids) > self.max_genomes_for_stats:
cids = set(random.sample(cids, self.max_genomes_for_stats))
# calculate ANI to representative genome
gid_pairs = []
gids = list(cids.union([rid]))
for gid1, gid2 in combinations(gids, 2):
gid_pairs.append((gid1, gid2))
gid_pairs.append((gid2, gid1))
if True: # ***DEBUGGING
ani_af = self.fastani.pairs(gid_pairs,
genome_files,
report_progress=False)
else:
ani_af = self.fastani.ani_cache
# calculate medoid point
if len(gids) > 2:
dist_mat = np_zeros((len(gids), len(gids)))
for i, gid1 in enumerate(gids):
for j, gid2 in enumerate(gids):
if i < j:
ani, _af = FastANI.symmetric_ani(
ani_af, gid1, gid2)
dist_mat[i, j] = 100 - ani
dist_mat[j, i] = 100 - ani
medoid_idx = np_argmin(dist_mat.sum(axis=0))
medoid_gid = gids[medoid_idx]
else:
# with only 2 genomes in a cluster, the representative is the
# natural medoid at least for reporting statistics for the
# individual species cluster
medoid_gid = rid
mean_ani_to_medoid = np_mean([FastANI.symmetric_ani(ani_af, gid, medoid_gid)[0]
for gid in gids if gid != medoid_gid])
mean_ani_to_rep = np_mean([FastANI.symmetric_ani(ani_af, gid, rid)[0]
for gid in gids if gid != rid])
if mean_ani_to_medoid < mean_ani_to_rep:
self.logger.error('mean_ani_to_medoid < mean_ani_to_rep')
sys.exit(-1)
# calculate statistics
anis = []
for gid1, gid2 in combinations(gids, 2):
ani, _af = FastANI.symmetric_ani(ani_af, gid1, gid2)
anis.append(ani)
stats[rid] = self.PairwiseStats(
min_ani=min(anis),
mean_ani=np_mean(anis),
std_ani=np_std(anis),
median_ani=np_median(anis),
ani_to_medoid=FastANI.symmetric_ani(
ani_af, rid, medoid_gid)[0],
mean_ani_to_medoid=mean_ani_to_medoid,
mean_ani_to_rep=mean_ani_to_rep,
ani_below_95=sum([1 for ani in anis if ani < 95]))
sys.stdout.write('\n')
return stats
def intragenus_pairwise_ani(self, clusters, species, genome_files, gtdb_taxonomy):
"""Determine pairwise intra-genus ANI between representative genomes."""
self.logger.info(
'Calculating pairwise intra-genus ANI values between GTDB representatives.')
# get genus for each representative
genus = {}
for rid, sp in species.items():
genus[rid] = sp.split()[0].replace('s__', '')
assert genus[rid] == gtdb_taxonomy[rid][5].replace('g__', '')
# get pairs above Mash threshold
self.logger.info('Determining intra-genus genome pairs.')
ani_pairs = []
for qid in clusters:
for rid in clusters:
if qid == rid:
continue
genusA = genus[qid]
genusB = genus[rid]
if genusA != genusB:
continue
ani_pairs.append((qid, rid))
ani_pairs.append((rid, qid))
self.logger.info(
'Identified {:,} intra-genus genome pairs.'.format(len(ani_pairs)))
# calculate ANI between pairs
self.logger.info(
'Calculating ANI between {:,} genome pairs:'.format(len(ani_pairs)))
if True: # ***DEBUGGING
ani_af = self.fastani.pairs(ani_pairs, genome_files)
pickle.dump(ani_af, open(os.path.join(
self.output_dir, 'type_genomes_ani_af.pkl'), 'wb'))
else:
ani_af = pickle.load(
open(os.path.join(self.output_dir, 'type_genomes_ani_af.pkl'), 'rb'))
# find closest intra-genus pair for each rep
fout = open(os.path.join(self.output_dir,
'intra_genus_pairwise_ani.tsv'), 'w')
fout.write(
'Genus\tSpecies 1\tGenome ID 1\tSpecies 2\tGenome ID2\tANI\tAF\n')
closest_intragenus_rep = {}
for qid in clusters:
genusA = genus[qid]
closest_ani = 0
closest_af = 0
closest_gid = None
for rid in clusters:
if qid == rid:
continue
genusB = genus[rid]
if genusA != genusB:
continue
ani, af = ('n/a', 'n/a')
if qid in ani_af and rid in ani_af[qid]:
ani, af = FastANI.symmetric_ani(ani_af, qid, rid)
if ani > closest_ani:
closest_ani = ani
closest_af = af
closest_gid = rid
fout.write('{}\t{}\t{}\t{}\t{}\t{}\t{}\n'.format(
genusA,
species[qid],
qid,
species[rid],
rid,
ani,
af))
if closest_gid:
closest_intragenus_rep[qid] = (
closest_gid, closest_ani, closest_af)
fout.close()
# write out closest intra-genus species to each representative
fout = open(os.path.join(self.output_dir,
'closest_intragenus_rep.tsv'), 'w')
fout.write(
'Genome ID\tSpecies\tIntra-genus neighbour\tIntra-genus species\tANI\tAF\n')
for qid in closest_intragenus_rep:
rid, ani, af = closest_intragenus_rep[qid]
fout.write('%s\t%s\t%s\t%s\t%.2f\t%.3f\n' % (
qid,
species[qid],
rid,
species[rid],
ani,
af))
fout.close()
def dereplicate_species(self, species, rid, cids, genomes, mash_out_dir):
"""Dereplicate genomes within a GTDB species."""
# greedily dereplicate genomes based on genome priority
sorted_gids = self.order_genomes_by_priority(cids.difference([rid]),
genomes)
sorted_gids = [rid] + sorted_gids
# calculate Mash ANI between genomes
mash_ani = []
if len(sorted_gids) > 1:
# calculate MASH distances between genomes
out_prefix = os.path.join(mash_out_dir,
species[3:].lower().replace(' ', '_'))
mash_ani = self.mash_sp_ani(sorted_gids, genomes, out_prefix)
# perform initial dereplication using Mash for species with excessive
# numbers of genomes
if len(sorted_gids) > self.max_genomes_per_sp:
self.logger.info(
' - limiting species to <={:,} genomes based on priority and Mash dereplication.'
.format(self.max_genomes_per_sp))
prev_mash_rep_gids = None
for ani_threshold in [
99.75, 99.5, 99.25, 99.0, 98.75, 98.5, 98.25, 98.0, 97.75,
97.5, 97.0, 96.5, 96.0, 95.0, None
]:
if ani_threshold is None:
self.logger.warning(
' - delected {:,} highest priority genomes from final Mash dereplication.'
% self.max_genomes_per_sp)
sorted_gids = mash_rep_gids[0:self.max_genomes_per_sp]
break
mash_rep_gids = self.mash_sp_dereplicate(
mash_ani, sorted_gids, ani_threshold)
self.logger.info(
' - dereplicated {} from {:,} to {:,} genomes at {:.2f}% ANI using Mash.'
.format(species, len(cids), len(mash_rep_gids),
ani_threshold))
if len(mash_rep_gids) <= self.max_genomes_per_sp:
if not prev_mash_rep_gids:
# corner case where dereplication is occurring at 99.75%
prev_mash_rep_gids = sorted_gids
# select maximum allowed number of genomes by taking all genomes in the
# current Mash dereplicated set and then the highest priority genomes in the
# previous Mash dereplicated set which have not been selected
cur_sel_gids = set(mash_rep_gids)
prev_sel_gids = set(prev_mash_rep_gids)
num_prev_to_sel = self.max_genomes_per_sp - len(
cur_sel_gids)
num_prev_selected = 0
sel_sorted_gids = []
for gid in sorted_gids:
if gid in cur_sel_gids:
sel_sorted_gids.append(gid)
elif (gid in prev_sel_gids
and num_prev_selected < num_prev_to_sel):
num_prev_selected += 1
sel_sorted_gids.append(gid)
if len(sel_sorted_gids) == self.max_genomes_per_sp:
break
assert len(cur_sel_gids - set(sel_sorted_gids)) == 0
assert num_prev_to_sel == num_prev_selected
assert len(sel_sorted_gids) == self.max_genomes_per_sp
sorted_gids = sel_sorted_gids
self.logger.info(
' - selected {:,} highest priority genomes from Mash dereplication at an ANI = {:.2f}%.'
.format(len(sorted_gids), ani_threshold))
break
prev_mash_rep_gids = mash_rep_gids
prev_ani_threshold = ani_threshold
# calculate FastANI ANI/AF between genomes passing Mash filtering
ani_pairs = set()
for gid1, gid2 in permutations(sorted_gids, 2):
if gid1 in mash_ani and gid2 in mash_ani[gid1]:
if mash_ani[gid1][gid2] >= self.min_mash_intra_sp_ani:
ani_pairs.add((gid1, gid2))
ani_pairs.add((gid2, gid1))
self.logger.info(
' - calculating FastANI between {:,} pairs with Mash ANI >= {:.1f}%.'
.format(len(ani_pairs), self.min_mash_intra_sp_ani))
ani_af = self.fastani.pairs(ani_pairs,
genomes.genomic_files,
report_progress=False,
check_cache=True)
self.fastani.write_cache(silence=True)
# perform greedy dereplication
sp_reps = []
for idx, gid in enumerate(sorted_gids):
# determine if genome clusters with existing representative
clustered = False
for rid in sp_reps:
ani, af = FastANI.symmetric_ani(ani_af, gid, rid)
if ani >= self.derep_ani and af >= self.derep_af:
clustered = True
break
if not clustered:
sp_reps.append(gid)
self.logger.info(
' - dereplicated {} from {:,} to {:,} genomes.'.format(
species, len(sorted_gids), len(sp_reps)))
# assign clustered genomes to most similar representative
subsp_clusters = {}
for rid in sp_reps:
subsp_clusters[rid] = [rid]
non_rep_gids = set(sorted_gids) - set(sp_reps)
for gid in non_rep_gids:
closest_rid = None
max_ani = 0
max_af = 0
for rid in sp_reps:
ani, af = FastANI.symmetric_ani(ani_af, gid, rid)
if ((ani > max_ani and af >= self.derep_af) or
(ani == max_ani and af >= max_af and af >= self.derep_af)):
max_ani = ani
max_af = af
closest_rid = rid
assert closest_rid is not None
subsp_clusters[closest_rid].append(gid)
return subsp_clusters
def write_synonym_table(self, type_strain_synonyms, consensus_synonyms,
ani_af, sp_priority_ledger, genus_priority_ledger,
lpsn_gss_file):
"""Create table indicating species names that should be considered synonyms."""
sp_priority_mngr = SpeciesPriorityManager(sp_priority_ledger,
genus_priority_ledger,
lpsn_gss_file,
self.output_dir)
out_file = os.path.join(self.output_dir, 'synonyms.tsv')
fout = open(out_file, 'w')
fout.write(
'Synonym type\tNCBI species\tGTDB representative\tStrain IDs\tType sources\tPriority year'
)
fout.write('\tGTDB type species\tGTDB type strain\tNCBI assembly type')
fout.write(
'\tNCBI synonym\tHighest-quality synonym genome\tSynonym strain IDs\tSynonym type sources\tSynonym priority year'
)
fout.write(
'\tSynonym GTDB type species\tSynonym GTDB type strain\tSynonym NCBI assembly type'
)
fout.write('\tANI\tAF\tWarnings\n')
incorrect_priority = 0
failed_type_strain_priority = 0
for synonyms, synonym_type in [
(type_strain_synonyms, 'TYPE_STRAIN_SYNONYM'),
(consensus_synonyms, 'MAJORITY_VOTE_SYNONYM')
]:
for rid, synonym_ids in synonyms.items():
for gid in synonym_ids:
ani, af = FastANI.symmetric_ani(ani_af, rid, gid)
fout.write(synonym_type)
fout.write('\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(
self.cur_genomes[rid].ncbi_taxa.species, rid,
','.join(sorted(self.cur_genomes[rid].strain_ids())),
','.join(
sorted(self.cur_genomes[rid].gtdb_type_sources())
).upper().replace('STRAININFO', 'StrainInfo'),
sp_priority_mngr.species_priority_year(
self.cur_genomes,
rid), self.cur_genomes[rid].is_gtdb_type_species(),
self.cur_genomes[rid].is_gtdb_type_strain(),
self.cur_genomes[rid].ncbi_type_material))
synonym_priority_year = sp_priority_mngr.species_priority_year(
self.cur_genomes, gid)
if synonym_priority_year == Genome.NO_PRIORITY_YEAR:
synonym_priority_year = 'n/a'
fout.write('\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(
self.cur_genomes[gid].ncbi_taxa.species, gid,
','.join(sorted(self.cur_genomes[gid].strain_ids())),
','.join(
sorted(self.cur_genomes[gid].gtdb_type_sources())
).upper().replace('STRAININFO',
'StrainInfo'), synonym_priority_year,
self.cur_genomes[gid].is_gtdb_type_species(),
self.cur_genomes[gid].is_gtdb_type_strain(),
self.cur_genomes[gid].ncbi_type_material))
fout.write('\t{:.3f}\t{:.4f}'.format(ani, af))
if self.cur_genomes[rid].is_effective_type_strain(
) and self.cur_genomes[gid].is_effective_type_strain():
priority_gid, note = sp_priority_mngr.species_priority(
self.cur_genomes, rid, gid)
if priority_gid != rid:
incorrect_priority += 1
fout.write('\tIncorrect priority: {}'.format(note))
elif not self.cur_genomes[rid].is_gtdb_type_strain(
) and self.cur_genomes[gid].is_gtdb_type_strain():
failed_type_strain_priority += 1
fout.write(
'\tFailed to prioritize type strain of species')
fout.write('\n')
if incorrect_priority:
self.logger.warning(
f' - identified {incorrect_priority:,} synonyms with incorrect priority.'
)
if failed_type_strain_priority:
self.logger.warning(
f' - identified {failed_type_strain_priority:,} synonyms that failed to priotize the type strain of the species.'
)
def cluster_genomes(self,
cur_genomes,
de_novo_rep_gids,
named_rep_gids,
final_cluster_radius):
"""Cluster new representatives to representatives of named GTDB species clusters."""
all_reps = de_novo_rep_gids.union(named_rep_gids)
nonrep_gids = set(cur_genomes.genomes.keys()) - all_reps
self.logger.info('Clustering {:,} genomes to {:,} named and de novo representatives.'.format(
len(nonrep_gids), len(all_reps)))
if True: # ***
# calculate MASH distance between non-representatives and representatives genomes
mash = Mash(self.cpus)
mash_rep_sketch_file = os.path.join(
self.output_dir, 'gtdb_rep_genomes.msh')
rep_genome_list_file = os.path.join(
self.output_dir, 'gtdb_rep_genomes.lst')
mash.sketch(all_reps, cur_genomes.genomic_files,
rep_genome_list_file, mash_rep_sketch_file)
mash_none_rep_sketch_file = os.path.join(
self.output_dir, 'gtdb_nonrep_genomes.msh')
non_rep_file = os.path.join(
self.output_dir, 'gtdb_nonrep_genomes.lst')
mash.sketch(nonrep_gids, cur_genomes.genomic_files,
non_rep_file, mash_none_rep_sketch_file)
# get Mash distances
mash_dist_file = os.path.join(
self.output_dir, 'gtdb_rep_vs_nonrep_genomes.dst')
mash.dist(float(100 - self.min_mash_ani)/100,
mash_rep_sketch_file,
mash_none_rep_sketch_file,
mash_dist_file)
# read Mash distances
mash_ani = mash.read_ani(mash_dist_file)
# calculate ANI between non-representatives and representatives genomes
clusters = {}
for gid in all_reps:
clusters[gid] = []
mash_ani_pairs = []
for qid in mash_ani:
assert qid in nonrep_gids
for rid in mash_ani[qid]:
assert rid in all_reps
if (mash_ani[qid][rid] >= self.min_mash_ani
and qid != rid):
mash_ani_pairs.append((qid, rid))
mash_ani_pairs.append((rid, qid))
self.logger.info('Calculating ANI between {:,} species clusters and {:,} unclustered genomes ({:,} pairs):'.format(
len(clusters),
len(nonrep_gids),
len(mash_ani_pairs)))
ani_af = self.fastani.pairs(
mash_ani_pairs, cur_genomes.genomic_files)
# assign genomes to closest representatives
# that is within the representatives ANI radius
self.logger.info('Assigning genomes to closest representative.')
for idx, cur_gid in enumerate(nonrep_gids):
closest_rep_gid = None
closest_rep_ani = 0
closest_rep_af = 0
for rep_gid in clusters:
ani, af = FastANI.symmetric_ani(ani_af, cur_gid, rep_gid)
isclose_abs_tol = 1e-4
if (ani >= final_cluster_radius[rep_gid].ani - isclose_abs_tol
and af >= self.af_sp - isclose_abs_tol):
# the isclose_abs_tol factor is used in order to avoid missing genomes due to
# small rounding errors when comparing floating point values. In particular,
# the ANI radius for named GTDB representatives is read from file so small
# rounding errors could occur. This has only been observed once, but seems
# like good practice to use isclose here.
if ani > closest_rep_ani or (ani == closest_rep_ani and af > closest_rep_af):
closest_rep_gid = rep_gid
closest_rep_ani = ani
closest_rep_af = af
if closest_rep_gid:
clusters[closest_rep_gid].append(ClusteredGenome(gid=cur_gid,
ani=closest_rep_ani,
af=closest_rep_af))
else:
self.logger.warning(
'Failed to assign genome {} to representative.'.format(cur_gid))
if closest_rep_gid:
self.logger.warning(
' - closest_rep_gid = {}'.format(closest_rep_gid))
self.logger.warning(
' - closest_rep_ani = {:.2f}'.format(closest_rep_ani))
self.logger.warning(
' - closest_rep_af = {:.2f}'.format(closest_rep_af))
self.logger.warning(
' - closest rep radius = {:.2f}'.format(final_cluster_radius[closest_rep_gid].ani))
else:
self.logger.warning(
' - no representative with an AF >{:.2f} identified.'.format(self.af_sp))
statusStr = '-> Assigned {:,} of {:,} ({:.2f}%) genomes.'.format(idx+1,
len(nonrep_gids),
float(idx+1)*100/len(nonrep_gids)).ljust(86)
sys.stdout.write('{}\r'.format(statusStr))
sys.stdout.flush()
sys.stdout.write('\n')
pickle.dump(clusters, open(os.path.join(
self.output_dir, 'clusters.pkl'), 'wb'))
pickle.dump(ani_af, open(os.path.join(self.output_dir,
'ani_af_rep_vs_nonrep.de_novo.pkl'), 'wb'))
else:
self.logger.warning(
'Using previously calculated results in: {}'.format('clusters.pkl'))
clusters = pickle.load(
open(os.path.join(self.output_dir, 'clusters.pkl'), 'rb'))
self.logger.warning('Using previously calculated results in: {}'.format(
'ani_af_rep_vs_nonrep.de_novo.pkl'))
ani_af = pickle.load(
open(os.path.join(self.output_dir, 'ani_af_rep_vs_nonrep.de_novo.pkl'), 'rb'))
return clusters, ani_af
def selected_rep_genomes(self,
cur_genomes,
nonrep_radius,
unclustered_qc_gids,
mash_ani):
"""Select de novo representatives for species clusters in a greedy fashion using species-specific ANI thresholds."""
# sort genomes by quality score
self.logger.info(
'Selecting de novo representatives in a greedy manner based on quality.')
q = {gid: cur_genomes[gid].score_type_strain()
for gid in unclustered_qc_gids}
q_sorted = sorted(q.items(), key=lambda kv: (
kv[1], kv[0]), reverse=True)
# greedily determine representatives for new species clusters
cluster_rep_file = os.path.join(self.output_dir, 'cluster_reps.tsv')
clusters = set()
if not os.path.exists(cluster_rep_file):
clustered_genomes = 0
max_ani_pairs = 0
for idx, (cur_gid, _score) in enumerate(q_sorted):
# determine reference genomes to calculate ANI between
ani_pairs = []
if cur_gid in mash_ani:
for rep_gid in clusters:
if mash_ani[cur_gid].get(rep_gid, 0) >= self.min_mash_ani:
ani_pairs.append((cur_gid, rep_gid))
ani_pairs.append((rep_gid, cur_gid))
# determine if genome clusters with representative
clustered = False
if ani_pairs:
if len(ani_pairs) > max_ani_pairs:
max_ani_pairs = len(ani_pairs)
ani_af = self.fastani.pairs(
ani_pairs, cur_genomes.genomic_files, report_progress=False)
closest_rep_gid = None
closest_rep_ani = 0
closest_rep_af = 0
for rep_gid in clusters:
ani, af = FastANI.symmetric_ani(
ani_af, cur_gid, rep_gid)
if af >= self.af_sp:
if ani > closest_rep_ani or (ani == closest_rep_ani and af > closest_rep_af):
closest_rep_gid = rep_gid
closest_rep_ani = ani
closest_rep_af = af
if ani > nonrep_radius[cur_gid].ani and af >= self.af_sp:
nonrep_radius[cur_gid] = GenomeRadius(ani=ani,
af=af,
neighbour_gid=rep_gid)
if closest_rep_gid and closest_rep_ani > nonrep_radius[closest_rep_gid].ani:
clustered = True
if not clustered:
# genome is a new species cluster representative
clusters.add(cur_gid)
else:
clustered_genomes += 1
if (idx+1) % 10 == 0 or idx+1 == len(q_sorted):
statusStr = '-> Clustered {:,} of {:,} ({:.2f}%) genomes [ANI pairs: {:,}; clustered genomes: {:,}; clusters: {:,}].'.format(
idx+1,
len(q_sorted),
float(idx+1)*100/len(q_sorted),
max_ani_pairs,
clustered_genomes,
len(clusters)).ljust(96)
sys.stdout.write('{}\r'.format(statusStr))
sys.stdout.flush()
max_ani_pairs = 0
sys.stdout.write('\n')
# write out selected cluster representative
fout = open(cluster_rep_file, 'w')
for gid in clusters:
fout.write('{}\n'.format(gid))
fout.close()
else:
# read cluster reps from file
self.logger.warning(
'Using previously determined cluster representatives.')
for line in open(cluster_rep_file):
gid = line.strip()
clusters.add(gid)
self.logger.info(
'Selected {:,} representative genomes for de novo species clusters.'.format(len(clusters)))
return clusters
def run(self, gtdb_metadata_file, genomic_path_file):
"""Dereplicate GTDB species clusters using ANI/AF criteria."""
# create GTDB genome sets
self.logger.info('Creating GTDB genome set.')
genomes = Genomes()
genomes.load_from_metadata_file(gtdb_metadata_file)
genomes.load_genomic_file_paths(genomic_path_file)
self.logger.info(
' - genome set has {:,} species clusters spanning {:,} genomes.'.
format(len(genomes.sp_clusters),
genomes.sp_clusters.total_num_genomes()))
# get GTDB representatives from same genus
self.logger.info('Identifying GTDB representatives in the same genus.')
genus_gids = defaultdict(list)
num_reps = 0
for gid in genomes:
if not genomes[gid].gtdb_is_rep:
continue
gtdb_genus = genomes[gid].gtdb_taxa.genus
genus_gids[gtdb_genus].append(gid)
num_reps += 1
self.logger.info(
f' - identified {len(genus_gids):,} genera spanning {num_reps:,} representatives'
)
# get all intragenus comparisons
self.logger.info('Determining all intragenus comparisons.')
gid_pairs = []
for gids in genus_gids.values():
if len(gids) < 2:
continue
for g1, g2 in permutations(gids, 2):
gid_pairs.append((g1, g2))
self.logger.info(
f' - identified {len(gid_pairs):,} intragenus comparisons')
# calculate FastANI ANI/AF between target genomes
self.logger.info('Calculating ANI between intragenus pairs.')
ani_af = self.fastani.pairs(gid_pairs,
genomes.genomic_files,
report_progress=True,
check_cache=True)
self.fastani.write_cache(silence=True)
# write out results
fout = open(
os.path.join(self.output_dir, 'intragenus_ani_af_reps.tsv'), 'w')
fout.write(
'Query ID\tQuery species\tTarget ID\tTarget species\tANI\tAF\n')
for qid in ani_af:
for rid in ani_af:
ani, af = FastANI.symmetric_ani(ani_af, qid, rid)
fout.write('{}\t{}\t{}\t{}\t{:.3f}\t{:.3f}\n'.format(
qid, genomes[qid].gtdb_taxa.species, rid,
genomes[rid].gtdb_taxa.species, ani, af))
fout.close()