def get_patric_annotation_files():
#pathway_directory = '%spatric_pathway/' % (parse_midas_data.data_directory)
os.system("mkdir -p %s" % patric_directory)
#os.system("mkdir -p %s" % pathway_directory)
#intermediate_filename = intermediate_filename_template % (pairwise_directory, species_name)
# get a list of specis to run this script on.
good_species_list = parse_midas_data.parse_good_species_list()
for species_name in good_species_list:
core_genes = core_gene_utils.parse_core_genes(species_name)
genome_ids = set([
".".join(core_gene.split(".", 2)[:2]) for core_gene in core_genes
])
for genome_id in genome_ids:
print(species_name, genome_id)
cmnd_subsystem = "curl ftp://ftp.patricbrc.org/genomes/%s/%s.PATRIC.subsystem.tab -o %s/%s.PATRIC.subsystem.tab" % (
genome_id, genome_id, patric_directory, genome_id)
cmnd_pathway = "curl ftp://ftp.patricbrc.org/genomes/%s/%s.PATRIC.pathway.tab -o %s/%s.PATRIC.pathway.tab" % (
genome_id, genome_id, patric_directory, genome_id)
cmnd_features = "curl ftp://ftp.patricbrc.org/genomes/%s/%s.PATRIC.features.tab -o %s/%s.PATRIC.features.tab" % (
genome_id, genome_id, patric_directory, genome_id)
cmnd_cat = "cat %s/%s.PATRIC.features.tab | cut -f6,21 > %s/%s.kegg.txt" % (
patric_directory, genome_id, patric_directory, genome_id)
cmnd_bzip2 = "bzip2 -k %s/%s.kegg.txt" % (patric_directory,
genome_id)
cmnd_rRNAs = "curl ftp://ftp.patricbrc.org/genomes/%s/%s.PATRIC.frn -o %s/%s.PATRIC.frn" % (
genome_id, genome_id, patric_directory, genome_id)
def load_centroid_gene_map(desired_species_name=None):
if desired_species_name == None:
import parse_midas_data
desired_speciess = parse_midas_data.parse_good_species_list()
else:
desired_speciess = [desired_species_name]
for desired_species_name in desired_speciess:
# First load reference genes
reference_genes = load_reference_genes(desired_species_name)
gene_info_file = gzip.open(
"%span_genomes/%s/gene_info.txt.gz" %
(config.midas_directory, desired_species_name), 'r')
gene_info_file.readline() # header
centroid_gene_map = {}
for line in gene_info_file:
items = line.split("\t")
gene_id = items[0].strip()
centroid_id = items[3].strip()
if centroid_id not in centroid_gene_map:
centroid_gene_map[centroid_id] = centroid_id
if (gene_id in reference_genes) and (centroid_id
not in reference_genes):
centroid_gene_map[centroid_id] = gene_id
gene_info_file.close()
return centroid_gene_map
################################################################################
min_sample_size = config.between_host_min_sample_size # 46 gives at least 1000
low_divergence_threshold = config.between_low_divergence_threshold
allowed_variant_types = set(['4D'])
#focal_speciess = ['Bacteroides_vulgatus_57955', 'Roseburia_inulinivorans_61943']
#focal_speciess = ['Bacteroides_vulgatus_57955', 'Faecalibacterium_prausnitzii_62201']
focal_speciess = ['Bacteroides_vulgatus_57955', 'Akkermansia_muciniphila_55290']
focal_colors = ['b','g']
#supplemental_focal_species = ['Bacteroides_fragilis_54507', 'Alistipes_putredinis_61533', 'Eubacterium_rectale_56927']
supplemental_focal_species = ['Bacteroides_fragilis_54507', 'Parabacteroides_distasonis_56985', 'Alistipes_shahii_62199'] # 'Ruminococcus_bromii_62047']
good_species_list = parse_midas_data.parse_good_species_list()
if debug:
good_species_list = good_species_list[0:2]
sys.stderr.write("Loading sample metadata...\n")
subject_sample_map = sample_utils.parse_subject_sample_map()
sample_continent_map = sample_utils.parse_sample_continent_map()
sys.stderr.write("Done!\n")
####################################################
#
# Set up Figure (2 panels, arranged in 2x1 grid)
#
####################################################
pylab.figure(1,figsize=(5,6))
import parse_midas_data
if len(sys.argv) < 3:
sys.stderr.write(
"Usage: python loop_over_species_wrapper.py all|debug|species command...\n"
)
sys.exit(1)
# First argument is either 'all', 'debug', or a species name
debug_flag = ""
if sys.argv[1] == 'debug':
species_names = [parse_midas_data.debug_species_name]
debug_flag = "--debug"
elif sys.argv[1] == 'all':
species_names = parse_midas_data.parse_good_species_list()
else:
good_species_names = parse_midas_data.parse_good_species_list()
species_names = []
pattern = sys.argv[1]
for species_name in good_species_names:
if species_name.startswith(pattern):
species_names.append(species_name)
# Remaining arguments are command to run, with species name appended as last argument
command = " ".join(sys.argv[2:])
sys.stderr.write("Running command: %s\n" % command)
sys.stderr.write("for %d species...\n\n" % len(species_names))
for species_name in species_names:
f = float(items[1])
gene_freq_map[gene_name] = f
file.close()
return gene_freq_map
# Actually calculate the core genes
if __name__ == '__main__':
import parse_midas_data
os.system('mkdir -p %s' % core_genes_directory)
os.system('mkdir -p %s' % external_core_genes_directory)
pangenome_species = parse_midas_data.parse_good_species_list()
cmin = config.core_genome_min_copynum
cmax = config.core_genome_max_copynum
shared_cmin = config.shared_genome_min_copynum
min_good_fraction = config.core_genome_min_prevalence
min_coverage = 5 # (for assessing core genome, we'll use a lower coverage value than when we look at real changes)
output_filename = default_core_gene_filename
output_file = gzip.GzipFile(output_filename, "w")
stringent_output_filename = default_stringent_core_gene_filename
stringent_output_file = gzip.GzipFile(stringent_output_filename, "w")
shared_output_file = gzip.GzipFile(default_shared_gene_filename, "w")
def get_16S_fasta():
good_species_list = parse_midas_data.parse_good_species_list()
frn = open(frn_path, 'w')
for species_name in good_species_list:
core_genes = core_gene_utils.parse_core_genes(species_name)
genome_ids = list(
set([
".".join(core_gene.split(".", 2)[:2])
for core_gene in core_genes
]))
genome_id = genome_ids[0]
species_name_frn_path = "%s/%s.PATRIC.frn" % (patric_directory,
genome_id)
species_name_frn = classFASTA(species_name_frn_path).readFASTA()
counted_rRNA = False
for species_name_frn_name, species_name_frn_seq in species_name_frn:
if 'ssuRNA' not in species_name_frn_name:
continue
if len(species_name_frn_seq) < 1200:
continue
if counted_rRNA == False:
#print(species_name, species_name_frn_name, len(species_name_frn_seq))
species_name_frn_name_split = species_name_frn_name.split()
frn_header = species_name + '|' + species_name_frn_name_split[
0].split('|')[1]
frn.write('>%s\n' % frn_header)
seq_split = [
species_name_frn_seq[i:i + 80]
for i in range(0, len(species_name_frn_seq), 80)
]
for seq in seq_split:
frn.write('%s\n' % seq)
frn.write('\n')
counted_rRNA = True
else:
continue
# same for outgroup
outgroup = classFASTA(outgroup_path).readFASTA()
print(outgroup[0][0])
frn.write('>%s\n' % outgroup[0][0])
seq_outgroup_split = [
outgroup[0][1][i:i + 80] for i in range(0, len(outgroup[0][1]), 80)
]
for seq in seq_outgroup_split:
frn.write('%s\n' % seq)
frn.write('\n')
frn.close()
os.system('muscle -in %s -out %s' % (frn_path, frn_aligned_path))
