def main(indir, odir, suffix, evalue, transpose, prefix, test):
indir = process_path(indir)
odir = process_path(odir)
gid2locus2ko = retrieve_info(indir, suffix, test_or_not=test)
post_filtered = filtration_part(gid2locus2ko, evalue)
if not exists(odir):
os.makedirs(odir)
if prefix is not None:
ofile_info = join(odir, f"{prefix}_info.tab")
ofile_binary = join(odir, f"{prefix}_binary.tab")
ofile_num = join(odir, f"{prefix}_num.tab")
else:
ofile_info = join(odir, "merged_hmm_info.tab")
ofile_binary = join(odir, "merged_hmm_binary.tab")
ofile_num = join(odir, "merged_hmm_num.tab")
tqdm.write("Complete filterations...")
tqdm.write(
"It need time to convert the generated dict into DataFrame. Be patient..."
)
final_df = pd.DataFrame.from_dict(post_filtered, orient='index')
bin_df = final_df.applymap(lambda x: 0 if pd.isna(x) else 1)
num_df = final_df.applymap(lambda x: 0
if pd.isna(x) else len(str(x).split(',')))
if transpose:
final_df = final_df.T
bin_df = bin_df.T
num_df = num_df.T
final_df.to_csv(ofile_info, sep='\t', index=1)
bin_df.to_csv(ofile_binary, sep='\t', index=1)
num_df.to_csv(ofile_num, sep='\t', index=1)
def main(indir, odir, evalue, transpose, test):
indir = process_path(indir)
odir = process_path(odir)
gid2locus2ko, exists_db = retrieve_info(indir, test=test)
locus2ko, sep_l2ko = filtration_part(gid2locus2ko, exists_db, evalue)
tqdm.write("Complete filterations...")
if not exists(odir):
os.makedirs(odir)
outut_for(locus2ko, odir, name='mixed', transpose=transpose)
for db, l2ko in sep_l2ko.items():
outut_for(l2ko,
join(odir, f'annotated_with_{db}'),
name=db,
transpose=transpose)
def main(indir, ofile):
indir = process_path(indir)
ofile = process_path(ofile)
if not exists(dirname(ofile)):
os.makedirs(dirname(ofile))
all_tsv = glob(join(indir, '*', 'storage', 'bin_stats_ext.tsv'))
result = {}
for each_f in tqdm(all_tsv):
text = open(each_f).read().strip('\n')
gid, v = text.split('\t')
v = eval(v)
v = {k: _v for k, _v in v.items() if not k.startswith('GCN')}
result[gid] = v
new_df = pd.DataFrame.from_dict(result, orient='index')
new_df.to_csv(ofile, sep='\t')
def process_IO(infile, out):
if out is None:
out = infile.rpartition('.')[0] + '.newick'
else:
out = process_path(out)
if not exists(dirname(out)):
os.makedirs(dirname(out))
return out
def main(indir, ofile, clean):
indir = process_path(indir)
ofile = process_path(ofile)
if not exists(dirname(ofile)):
os.makedirs(dirname(ofile))
all_tsv = glob(join(indir, "*", "storage", "bin_stats_ext.tsv"))
result = {}
for each_f in tqdm(all_tsv):
text = open(each_f).read().strip("\n")
gid, v = text.split("\t")
v = eval(v)
v = {k: _v for k, _v in v.items() if not k.startswith("GCN")}
result[gid] = v
new_df = pd.DataFrame.from_dict(result, orient="index")
if clean:
new_df = new_df.loc[:, remaining_columns]
new_df.to_csv(ofile, sep="\t")
def cli(intree_ori, mcmc_out_tree, output_dating_result_tree, itol_annotate,
root_with, tree_format):
output_dating_result_tree = process_path(output_dating_result_tree)
if itol_annotate is None:
itol_annotate = dirname(output_dating_result_tree)
itol_annotate = process_path(itol_annotate)
if ',' in str(root_with):
root_with = [_.strip() for _ in root_with.split(',')]
elif root_with is None:
pass
else:
root_with = [root_with.strip()]
if not os.path.exists(dirname(output_dating_result_tree)):
os.makedirs(dirname(output_dating_result_tree))
main(intree_ori,
mcmc_out_tree,
output_dating_result_tree,
itol_annotate=itol_annotate,
root_with=root_with,
tree_format=tree_format)
def cli(indir, ofile, suffix, num_percentage, num_genomes, genome_list):
if genome_list is None:
genome_list = join(indir, 'selected_genomes.txt')
with open(genome_list, 'r') as f1:
gids = f1.read().split('\n')
gids = [convert_genome_ID(_) for _ in gids]
if num_genomes is None and num_percentage is None:
num_percentage = 100
num_genomes = len(gids)
elif num_genomes is None:
num_percentage = int(num_percentage)
num_genomes = len(gids) * num_percentage / 100
else:
num_genomes = int(num_genomes)
genes = main(indir, suffix, num_genomes)
ofile = process_path(ofile)
if not exists(dirname(ofile)):
os.makedirs(dirname(ofile))
print(f"found {len(genes)} meet requirement.")
with open(ofile, 'w') as f1:
f1.write('\n'.join(genes))
def cli(indir, ofile, suffix, num_percentage, num_genes, num_total_genes,
not_add_prefix):
if num_genes is None and num_percentage is None:
num_percentage = 100
num_genes = num_total_genes
elif num_genes is None:
num_percentage = int(num_percentage)
num_genes = num_total_genes * num_percentage / 100
else:
num_genes = int(num_genes)
tqdm.write(f"Filter out genomes which only contain {num_genes} ")
if not_add_prefix is not None:
not_add_prefix_ids = [
_ for _ in open(not_add_prefix).read().split('\n') if _
]
else:
not_add_prefix_ids = []
genomes = main(indir, suffix, num_genes, not_add_prefix_ids)
ofile = process_path(ofile)
if not exists(dirname(ofile)):
os.makedirs(dirname(ofile))
with open(ofile, 'w') as f1:
f1.write('\n'.join(list(genomes)))
def main(indir,
outfile,
genome_list,
gene_list,
remove_identical,
seed,
concat_type,
graph,
fill_gaps,
suffix='aln',
fix_refseq=False,
not_add_prefix=None,
partition_method='genes',
simple_concat=False):
if genome_list is None:
genome_list = join(indir, 'selected_genomes.txt')
gids = open(genome_list, 'r').read().split('\n')
if simple_concat:
gids = set(gids)
else:
gids = [convert_genome_ID(_) for _ in gids if _]
if fix_refseq:
prefix = 'GCF_'
else:
prefix = 'GCA_'
if not_add_prefix is not None:
not_add_prefix_ids = [
_ for _ in open(not_add_prefix).read().split('\n') if _
]
else:
not_add_prefix_ids = []
# from GCA become locus_tag
record_pos_info = []
gid2record = {gid: '' for gid in gids}
las_pos = 0
order_seqs = sorted(glob(join(indir, f'*.{suffix}')))
if gene_list is not None:
if exists(str(gene_list)):
gene_list = [
_.strip() for _ in open(gene_list).read().split('\n') if _
]
order_seqs = [
_ for _ in order_seqs
if basename(_).replace(f'.{suffix}', '') in gene_list
]
elif isinstance(gene_list, str):
gene_list = [_.strip() for _ in gene_list.split(',') if _]
order_seqs = [
_ for _ in order_seqs
if basename(_).replace(f'.{suffix}', '') in gene_list
]
g2num_miss = {basename(_).replace(f'.{suffix}', ''): 0 for _ in order_seqs}
tqdm.write('itering all requested files ')
for idx, aln_file in tqdm(enumerate(order_seqs), total=len(order_seqs)):
aln_file_name = basename(aln_file).replace(f'.{suffix}', '')
aln_record = AlignIO.read(aln_file, format='fasta')
length_this_aln = aln_record.get_alignment_length()
# record the partition
name = "part%s" % int(idx + 1)
start, end = las_pos + 1, length_this_aln + las_pos
las_pos = end
record_pos_info.append((name, start, end, aln_record))
# done record
for gid in gid2record:
if simple_concat:
records = [_ for _ in aln_record if _.id == gid]
else:
records = [_ for _ in aln_record if _.id.split('_')[0] == gid]
if records:
gid2record[gid] += str(records[0].seq)
else:
gid2record[gid] += '-' * length_this_aln
g2num_miss[aln_file_name] += 1
if outfile is None:
outfile = join(indir, 'concat_aln.aln')
outpartition = join(indir, 'concat_aln.partition')
outphy = join(indir, 'concat_aln.phy')
ograph = join(indir, 'aln_stats.png')
else:
outfile = process_path(outfile)
if not exists(dirname(outfile)):
os.makedirs(dirname(outfile))
outpartition = outfile.rpartition('.')[0] + '.partition'
outphy = outfile.rpartition('.')[0] + '.phy'
ograph = join(dirname(outfile), 'aln_stats.png')
with open(outfile, 'w') as f1:
for gid, seq in gid2record.items():
if set(str(seq)) == {'-'}:
print(f"{gid} contains only gaps or missing data ")
continue
if simple_concat:
f1.write(f">{gid}\n")
else:
f1.write(
f'>{convert_genome_ID_rev(gid, prefix=prefix,not_add_prefix_ids=not_add_prefix_ids)}\n'
)
f1.write(f'{seq}\n')
if remove_identical:
remove_identical_seqs(outfile, seed=seed)
if concat_type.lower() in ['both', 'partition']:
generate_partition_file(outpartition, record_pos_info)
if concat_type.lower() in ['both', 'phy']:
gids = open(genome_list, 'r').read().split('\n')
if not simple_concat:
name_convertor = lambda x: convert_genome_ID_rev(
x, not_add_prefix_ids=not_add_prefix_ids)
else:
name_convertor = lambda x: x
generate_phy_file(outphy,
record_pos_info,
gids,
fill_gaps=fill_gaps,
remove_identical=remove_identical,
partition_method=partition_method,
name_convertor=name_convertor)
if graph:
generate_stats_graph(g2num_miss, total=len(gids), ofile=ograph)
def main(indir,
outfile,
genome_list,
gene_list,
concat_type,
graph,
fill_gaps,
suffix='aln',
fix_refseq=False,
remove_identical=False,
partition_method='genes',
simple_concat=True):
"""
The simple_concat indicate that name in `genome_list` is the genome name.
If it is False, it indicates that name in `genome_list` is converted/formatted genome name like the prefix of locus.
"""
if fix_refseq:
prefix = 'GCF_'
else:
prefix = 'GCA_'
# sampleing the genomes
name2prefix = get_genomes(genome_list,simple_concat)
# sampling the gene
order_seqs = get_genes(indir,suffix,gene_list)
# init parameters
g2num_miss = {basename(_).replace(f'.{suffix}', ''): 0 for _ in order_seqs}
# concat seqs
record_pos_info,name2record = concat_records(order_seqs,
name2prefix,
g2num_miss,
suffix,
simple_concat)
print(f"Found {len([k for k,v in g2num_miss.items() if v==0])} backbone genes")
if outfile is None and ',' not in indir:
outfile = join(indir, 'concat_aln.aln')
outpartition = join(indir, 'concat_aln.partition')
outphy = join(indir, 'concat_aln.phy')
ograph = join(indir, 'aln_stats.png')
else:
outfile = process_path(outfile)
if not exists(dirname(outfile)):
os.makedirs(dirname(outfile))
outpartition = outfile.rpartition('.')[0] + '.partition'
outphy = outfile.rpartition('.')[0] + '.phy'
ograph = join(dirname(outfile), 'aln_stats.png')
with open(outfile, 'w') as f1:
for final_name, seq in name2record.items():
if set(str(seq)) == {'-'}:
print(f"{final_name} contains only gaps or missing data ")
continue
if simple_concat:
f1.write(f">{final_name}\n")
else:
f1.write(f'>{convert_genome_ID_rev(final_name, prefix=prefix)}\n')
f1.write(f'{seq}\n')
if remove_identical:
remove_identical_seqs(outfile)
if concat_type.lower() in ['both', 'partition']:
generate_partition_file(outpartition, record_pos_info)
if concat_type.lower() in ['both', 'phy']:
gids = list(name2prefix)
def name_convertor(x):
tmp = [k for k,v in name2prefix.items() if x.split('_')[0] in v or x in v]
if not tmp:
return
else:
return tmp[0]
generate_phy_file(outphy,
record_pos_info,
gids,
fill_gaps=fill_gaps,
remove_identical=remove_identical,
partition_method=partition_method,
name_convertor=name_convertor)
if graph:
generate_stats_graph(g2num_miss, total=len(gids), ofile=ograph)