def intersect_ids(list_of_group_a, list_of_group_b, mode="common"):
# possible modes: common, only_a, only_b, not_common, combine, count
a = IdSet()
b = IdSet()
if mode == "common":
expression = lambda a, b: a & b
elif mode == "only_a":
expression = lambda a, b: a - b
elif mode == "only_b":
expression = lambda a, b: b - a
elif mode == "not_common":
expression = lambda a, b: a ^ b
elif mode == "combine":
expression = lambda a, b: a | b
for id_list in list_of_group_a:
a = a | IdSet(id_list)
for id_list in list_of_group_b:
b = b | IdSet(id_list)
if mode != "count":
return IdSet(expression(a, b))
else:
return len(a), len(b), len(a
& b), len(a -
b), len(b -
a), len(a
^ b), len(a
| b)
def get_cluster_names(clusters_dict, out_file=None, white_list_ids=None):
cluster_names = IdSet()
for species in clusters_dict:
species_clusters = IdSet(clusters_dict[species].keys())
cluster_names |= species_clusters
if out_file:
cluster_names.write(out_file)
return cluster_names & IdSet(
white_list_ids) if white_list_ids else cluster_names
def intersect_ids_from_files(files_with_ids_from_group_a,
files_with_ids_from_group_b,
result_file=None,
mode="common",
case_insensitive=False):
a = IdSet()
b = IdSet()
if mode == "common":
expression = lambda a, b: a & b
elif mode == "only_a":
expression = lambda a, b: a - b
elif mode == "only_b":
expression = lambda a, b: b - a
elif mode == "not_common":
expression = lambda a, b: a ^ b
elif mode == "combine":
expression = lambda a, b: a | b
#print(files_with_ids_from_group_a)
for filename in [files_with_ids_from_group_a] if isinstance(
files_with_ids_from_group_a,
str) else files_with_ids_from_group_a:
id_set = IdSet()
id_set.read(
filename,
comments_prefix="#",
expression=(lambda s: s.upper()) if case_insensitive else None)
a = a | id_set
for filename in [files_with_ids_from_group_b] if isinstance(
files_with_ids_from_group_b,
str) else files_with_ids_from_group_b:
id_set = IdSet()
id_set.read(
filename,
comments_prefix="#",
expression=(lambda s: s.upper()) if case_insensitive else None)
b = b | id_set
result_fd = open(result_file, "w") if result_file else sys.stdout
if mode != "count":
final_set = IdSet(expression(a, b))
final_set.write(result_fd)
else:
result_fd.write(
"Group_A\t%i\nGroup_B\t%i\nCommon\t%i\nOnly_group_A\t%i\nOnly_group_B\t%i\nNot_common\t%i\nAll\t%i\n"
% (len(a), len(b), len(a & b), len(a - b), len(b - a),
len(a ^ b), len(a | b)))
def extract_transcripts_by_ids(self, input_gff, transcript_id_file,
output_gff):
transcript_ids = IdSet()
transcript_ids.read(transcript_id_file, header=False)
GFF.write(
self.record_with_extracted_transcripts_generator(
input_gff, transcript_ids), open(output_gff, "w"))
def rename_scaffolds_in_gff(self,
input_gff,
syn_file,
output_prefix,
verbose=True):
syn_dict = SynDict(filename=syn_file)
skipped_id_list = IdSet()
output_gff = "%s.renamed.gff" % output_prefix
skipped_gff = "%s.skipped.gff" % output_prefix
skipped_id_file = "%s.skipped_scaffolds.ids" % output_prefix
with self.metaopen(input_gff, "r") as in_fd, \
self.metaopen(output_gff, "w") as out_fd, \
self.metaopen(skipped_gff, "w") as skipped_fd:
for line in in_fd:
if line[0] == "#":
out_fd.write(line)
gff_list = line.split("\t")
if gff_list[0] in syn_dict:
gff_list[0] = syn_dict[gff_list[0]]
out_fd.write("\t".join(gff_list))
else:
skipped_fd.write(line)
skipped_id_list.add(gff_list[0])
if verbose:
print("Not renamed scaffolds: %i" % len(skipped_id_list))
skipped_id_list.write(skipped_id_file)
def extract_monocluster_ids(self,
clusters_dict,
white_list_ids=None,
out_file=None):
"""
Extracts clusters with only one sequence in all species.
"""
monocluster_ids = IdSet()
cluster_names = self.get_cluster_names(clusters_dict)
for cluster_name in cluster_names:
for species in clusters_dict:
if white_list_ids:
if cluster_name not in white_list_ids:
break
if cluster_name not in clusters_dict[species]:
break
if len(clusters_dict[species][cluster_name]) > 1:
break
else:
monocluster_ids.add(cluster_name)
if out_file:
monocluster_ids.write(out_file)
return monocluster_ids
def get_sequence_names(clusters_dict,
write_ids=False,
out_prefix=None,
white_list_ids=None):
sequence_names_dict = SynDict()
for species in clusters_dict:
sequence_names_dict[species] = IdSet()
for species in clusters_dict:
for cluster_id in clusters_dict[species]:
if white_list_ids:
if cluster_id not in white_list_ids:
continue
sequence_names_dict[species] = sequence_names_dict[
species] | IdSet(clusters_dict[species][cluster_id])
if write_ids:
for species in clusters_dict:
out_file = "%s_%s.ids" % (
out_prefix, species) if out_prefix else "%s.ids" % species
sequence_names_dict[species].write(out_file)
return sequence_names_dict
def extract_monocluster_ids_from_file(self,
dir_with_cluster_files,
out_file,
file_with_white_list_ids=None):
# filenames are counted as species names
white_list_ids = None
if file_with_white_list_ids:
white_list_ids = IdSet()
white_list_ids.read(file_with_white_list_ids)
clusters_dict = self.read_cluster_files_from_dir(
dir_with_cluster_files)
monoclusters = self.extract_monocluster_ids(
clusters_dict, out_file=out_file, white_list_ids=white_list_ids)
return monoclusters
def get_scaffold_ids_from_gff(gff_file, out_file=None):
scaffold_id_set = IdSet()
with open(gff_file, "r") as gff_fd:
for line in gff_fd:
if line[0] == "#":
continue
scaffold_id = line.split("\t")[0]
scaffold_id_set.add(scaffold_id)
if out_file:
scaffold_id_set.write(out_file)
return scaffold_id_set
def get_column_value_set_from_file(self,
input_file,
column_number,
output_file=None,
separator="\t",
comments_prefix="#",
verbose=False):
column_value_set = IdSet([
line_list[column_number] for line_list in
self.file_line_as_list_generator(input_file,
separator=separator,
comments_prefix=comments_prefix)
])
if output_file:
column_value_set.write(output_file)
if verbose:
print("#Column %i (0-based) contains %i different values" %
(column_number, len(column_value_set)))
return column_value_set
def predict_genes(self,
output_prefix,
annotation_species_prefix,
genome_fasta,
augustus_species,
output_directory="./",
augustus_strand=None,
augustus_gene_model=None,
augustus_config_dir=None,
augustus_use_softmasking=None,
augustus_other_options="",
augustus_hintsfile=None,
augustus_extrinsicCfgFile=None,
augustus_predict_UTR=None,
augustus_min_intron_len=None,
threads=1,
augustus_dir="",
hmmer_dir="",
blast_dir="",
stop_codons_list=("TGA", "TAA", "TAG"),
genetic_code_table=1):
draft_file_prefix = "%s/raw/%s" % (output_directory, output_prefix)
augustus_splited_input_dir = "%s/splited_input/" % output_directory
augustus_splited_output_dir = "%s/splited_output_dir" % output_directory
output_raw_gff = "%s.raw.gff" % draft_file_prefix
output_gff = "%s.renamed.gff" % draft_file_prefix
augustus_pep = "%s.pep" % draft_file_prefix
AUGUSTUS.path = augustus_dir
AUGUSTUS.threads = threads
HMMER3.path = hmmer_dir
HMMER3.threads = threads
BLASTp.path = blast_dir
BLASTp.threads = threads
print("Annotating genes...")
AUGUSTUS.parallel_predict(
augustus_species,
genome_fasta,
output_raw_gff,
strand=augustus_strand,
gene_model=augustus_gene_model,
output_gff3=True,
other_options=augustus_other_options,
config_dir=augustus_config_dir,
use_softmasking=augustus_use_softmasking,
hints_file=augustus_hintsfile,
split_dir=augustus_splited_input_dir,
splited_output_dir=augustus_splited_output_dir,
extrinsicCfgFile=augustus_extrinsicCfgFile,
predict_UTR=augustus_predict_UTR,
combine_output_to_single_file=True,
min_intron_len=augustus_min_intron_len)
#replace_augustus_ids(augustus_gff, output_prefix, species_prefix=None, number_of_digits_in_id=8):
AUGUSTUS.replace_augustus_ids(output_raw_gff,
draft_file_prefix,
species_prefix=annotation_species_prefix,
number_of_digits_in_id=8)
#extract_transcript_sequences(self, input_gff_file, genomic_fasta_file, output_prefix, coding_only=False)
gffread_file_prefix = "%s.gffread" % draft_file_prefix
gffread_transcripts_file, gffread_cds_file, gffread_pep_file = Gffread.extract_transcript_sequences(
output_gff, genome_fasta, gffread_file_prefix)
gffread_trimmed_cds = ".".join(
gffread_cds_file.split(".")[:-1]) + ".trimmed.cds"
gffread_trimmed_pep = ".".join(
gffread_pep_file.split(".")[:-1]) + ".trimmed.pep"
self.trim_cds_and_remove_terminal_stop_codons(
gffread_cds_file,
gffread_trimmed_cds,
stop_codons_list=stop_codons_list
) # using default stop_codons(from universal genetic_code)/ Note that this will affect mtDNA proteins
inframe_stop_codons_file_prefix = "%s.inframe_stop_codon" % draft_file_prefix
self.translate_sequences_from_file(
gffread_trimmed_cds,
gffread_trimmed_pep,
format="fasta",
id_expression=None,
genetic_code_table=genetic_code_table,
translate_to_stop=False,
prefix_of_file_inframe_stop_codons_seqsin=
inframe_stop_codons_file_prefix) # Universal code !!!
AUGUSTUS.extract_gene_ids_from_output(output_gff,
all_annotated_genes_ids)
AUGUSTUS.extract_CDS_annotations_from_output(output_gff, CDS_gff)
print("Extracting peptides...")
AUGUSTUS.extract_proteins_from_output(
output_gff,
output_pep,
id_prefix="",
evidence_stats_file=output_evidence_stats,
supported_by_hints_file=output_supported_stats)
self.compare_sequences_from_files(output_pep,
"%s.trimmed.pep" % args.output,
"comparison_of_peptides",
format="fasta",
verbose=True)
os.system("awk -F'\\t' 'NR==1 {}; NR > 1 {print $2}' %s > %s" %
(output_supported_stats, output_supported_stats_ids))
print("Annotating domains(Pfam database)...")
HMMER3.parallel_hmmscan(
args.pfam_db,
output_pep,
output_hmmscan,
num_of_seqs_per_scan=None,
split_dir="splited_hmmscan_fasta/",
splited_output_dir="splited_hmmscan_output_dir",
tblout_outfile=None,
domtblout_outfile=output_domtblout,
pfamtblout_outfile=None,
splited_tblout_dir=None,
splited_domtblout_dir="hmmscan_domtblout/")
HMMER3.extract_dom_ids_hits_from_domtblout(
output_domtblout, output_pfam_annotated_dom_ids)
hits_dict = HMMER3.extract_dom_names_hits_from_domtblout(
output_domtblout, output_pfam_annotated_dom_names)
supported_ids = IdSet(hits_dict.keys())
supported_ids.write(output_pfam_supported_transcripts_ids)
remove_transcript_ids_str = "sed -re 's/\.t[0123456789]+//' %s | sort -k 1 | uniq > %s" % (
output_pfam_supported_transcripts_ids,
output_pfam_supported_genes_ids)
os.system(remove_transcript_ids_str)
print("Annotating peptides(Swissprot database)...")
BLASTp.parallel_blastp(output_pep,
args.swissprot_db,
evalue=0.0000001,
output_format=6,
outfile=output_swissprot_blastp_hits,
split_dir="splited_blastp_fasta",
splited_output_dir="splited_blastp_output_dir")
hits_dict = BLASTp.extract_hits_from_tbl_output(
output_swissprot_blastp_hits, output_swissprot_blastp_hits_names)
supported_ids = IdSet(hits_dict.keys())
supported_ids.write(output_swissprot_supported_transcripts_ids)
remove_transcript_ids_str = "sed -re 's/\.t[0123456789]+//' %s | sort -k 1 | uniq > %s" % (
output_swissprot_supported_transcripts_ids,
output_swissprot_supported_genes_ids)
os.system(remove_transcript_ids_str)
"""
accordance_file = "%s/%s.accordance" % (args.accordance_dir, species)
accordance_dict[species] = SynDict()
accordance_dict[species].read(accordance_file, key_index=1, value_index=0)
if args.name_first:
def split_name(pep_name):
gene_list = pep_name.split(args.name_separator)
return gene_list[0], args.name_separator.join(gene_list[1:])
else:
def split_name(pep_name):
gene_list = pep_name.split(args.name_separator)
return gene_list[-1], args.name_separator.join(gene_list[:-1])
families_with_errors = IdSet()
for family in pep_fam_dict:
cds_fam_dict[family] = []
for pep in pep_fam_dict[family]:
species, pep_name = split_name(pep)
if pep_name in accordance_dict[species]:
cds_name = "%s%s%s" % (species, args.name_separator, accordance_dict[species][pep_name]) if args.name_first else \
"%s%s%s" % (accordance_dict[species][pep_name], args.name_separator, species)
cds_fam_dict[family].append(cds_name)
else:
print("%s %s %s doesn't have associated cds in accordance file" % (family, species, pep_name))
families_with_errors.add(family)
for family in families_with_errors:
cds_fam_dict.pop(family, None)
(output_supported_stats, output_supported_stats_ids))
if args.pfam_db:
print("Annotating domains(Pfam database)...")
HMMER3.threads = args.threads
HMMER3.parallel_hmmscan(
args.pfam_db,
output_pep,
output_prefix_hmmscan,
"./", #output_hmmscan,
num_of_seqs_per_scan=None) # TODO CHECK!!!!!!!!!!!!!!!!!!!!!!!!!!
HMMER3.extract_dom_ids_hits_from_domtblout(output_domtblout,
output_pfam_annotated_dom_ids)
hits_dict = HMMER3.extract_dom_names_hits_from_domtblout(
output_domtblout, output_pfam_annotated_dom_names)
supported_ids = IdSet(hits_dict.keys())
supported_ids.write(output_pfam_supported_transcripts_ids)
remove_transcript_ids_str = "sed -re 's/\.t[0123456789]+//' %s | sort -k 1 | uniq > %s" % (
output_pfam_supported_transcripts_ids, output_pfam_supported_genes_ids)
os.system(remove_transcript_ids_str)
if args.swissprot_db:
print("Annotating peptides(Swissprot database)...")
BLASTp.threads = args.threads
BLASTp.parallel_blastp(output_pep,
args.swissprot_db,
evalue=0.0000001,
output_format=6,
outfile=output_swissprot_blastp_hits,
split_dir="splited_blastp_fasta",
splited_output_dir="splited_blastp_output_dir")
def filter_reference(self,
reference,
repeatmasking_gff_list,
output_prefix,
reference_len_file=None,
annotation_gff=None,
max_masked_fraction=0.8,
white_scaffold_list=(),
black_scaffold_list=(),
max_length=None):
scaffold_with_annotation_file = "%s.scaffolds_with_annotations.ids" % output_prefix
sorted_combined_repeatmasking_gff = "%s.sorted_combined_repeatmasking.gff" % output_prefix
reference_len_filename = "%s.reference.len" % output_prefix if reference_len_file is None else reference_len_file
repeatmasking_coverage_file = "%s.repeatmasking.coverage" % output_prefix
filtering_log_file = "%s.filtering.log" % output_prefix
filtered_scaffolds_file = "%s.filtered.fasta" % output_prefix
filtered_out_scaffolds_file = "%s.filtered_out.fasta" % output_prefix
filtered_out_scaffolds_id_file = "%s.filtered_out.ids" % output_prefix
scaffold_with_annotation_set = self.get_scaffold_ids_from_gff(
annotation_gff, out_file=scaffold_with_annotation_file)
print("Sorting GFFs with masking...")
sorting_string = "cat %s | sort -k1,1 -k4,4n -k5,5n > %s" % (
repeatmasking_gff_list if isinstance(repeatmasking_gff_list, str)
else " ".join(repeatmasking_gff_list),
sorted_combined_repeatmasking_gff)
self.execute(options=sorting_string, cmd="")
print("Parsing reference...")
reference_dict = self.parse_seq_file(reference, mode="parse")
if reference_len_file is None:
length_dict = self.get_lengths(reference_dict,
out_file=reference_len_filename)
else:
length_dict = SynDict(filename=reference_len_filename)
print("Calculating fraction of masked regions...")
GenomeCov.get_coverage_for_gff(sorted_combined_repeatmasking_gff,
reference_len_filename,
output=repeatmasking_coverage_file)
print("Filtering...")
low_zero_coverage_fraction_dict = SynDict(
filename=repeatmasking_coverage_file,
key_index=0,
value_index=4,
include_line_expression=lambda l: l.split("\t")[1] == "0",
expression=float,
include_value_expression=lambda v: v < (1.0 - max_masked_fraction))
#print low_zero_coverage_fraction_dict
scaffold_to_remove = IdSet()
with open(filtering_log_file, "w") as log_fd:
log_fd.write("#Scaffold\tStatus\tLength\tDescription\n")
for scaffold in reference_dict:
if scaffold in black_scaffold_list:
scaffold_to_remove.add(scaffold)
log_fd.write("%s\tRemoved\t%i\tBlackList\n" %
(scaffold, length_dict[scaffold]))
continue
if scaffold in low_zero_coverage_fraction_dict:
if scaffold in white_scaffold_list:
log_fd.write(
"%s\tRetained\t%i\tWhiteList,LowNonMaskedPercentage:%f\n"
% (scaffold, length_dict[scaffold],
low_zero_coverage_fraction_dict[scaffold]))
continue
if scaffold in scaffold_with_annotation_set:
log_fd.write(
"%s\tRetained\t%i\tWithAnnotations,LonNonMaskedPercentage:%f\n"
% (scaffold, length_dict[scaffold],
low_zero_coverage_fraction_dict[scaffold]))
continue
if not (max_length is None):
if length_dict[scaffold] > max_length:
log_fd.write(
"%s\tRetained\t%i\tLong,LowNonMaskedPercentage:%f\n"
% (scaffold, length_dict[scaffold],
low_zero_coverage_fraction_dict[scaffold]))
continue
scaffold_to_remove.add(scaffold)
log_fd.write(
"%s\tRemoved\t%i\tLowNonMaskedPercentage:%f\n" %
(scaffold, length_dict[scaffold],
low_zero_coverage_fraction_dict[scaffold]))
continue
log_fd.write("%s\tRetained\t%i\tOK\n" %
(scaffold, length_dict[scaffold]))
scaffold_to_remove.write(filename=filtered_out_scaffolds_id_file)
SeqIO.write(self.record_by_id_generator(reference_dict,
scaffold_to_remove),
filtered_out_scaffolds_file,
format="fasta")
SeqIO.write(self.record_by_id_generator(
reference_dict,
IdSet(reference_dict.keys()) - scaffold_to_remove),
filtered_scaffolds_file,
format="fasta")
print("Total scaffolds\t%i\nRemoved\t%i\n" %
(len(reference_dict), len(scaffold_to_remove)))
def extract_sequences_by_clusters(self,
dir_with_cluster_files,
dir_with_sequence_files,
output_dir,
file_with_white_list_cluster_ids=None,
mode="families",
sequence_file_extension="fasta",
sequence_file_format="fasta",
label_species=False,
separator_for_labeling="@",
species_label_first=True):
"""
basenames of cluster and sequence files must be same
mode:
clusters - extract sequences from clusters in separate files,
species - extract sequences from species to separate files
"""
# TODO: TEST IT BEFORE USAGE.WAS SIGNIFICANTLY CHANGED WITHOUT TESTING
white_list_ids = None
if file_with_white_list_cluster_ids:
white_list_ids = IdSet()
white_list_ids.read(file_with_white_list_cluster_ids)
clusters_dict = self.read_cluster_files_from_dir(
dir_with_cluster_files)
cluster_names = self.get_cluster_names(clusters_dict,
white_list_ids=white_list_ids)
sequence_super_dict = OrderedDict()
out_dir = self.check_path(output_dir)
for species in clusters_dict:
#idx_file = "%s_tmp.idx" % species
sequence_file = "%s%s.%s" % (self.check_path(
dir_with_sequence_files), species, sequence_file_extension)
sequence_super_dict[species] = CollectionSequence(
in_file=sequence_file,
format=sequence_file_format,
parsing_mode="parse")
#sequence_super_dict[species] = SeqIO.index_db(idx_file, sequence_file, format=sequence_file_format)
if mode == "species":
seqeuence_names = self.get_sequence_names(
clusters_dict,
write_ids=False,
out_prefix=None,
white_list_ids=white_list_ids)
for species in seqeuence_names:
out_file = "%s%s.%s" % (out_dir, species,
sequence_file_extension)
sequence_super_dict[species].write(
out_file, whitelist=seqeuence_names[species])
#SeqIO.write(SequenceRoutines.record_by_id_generator(sequence_super_dict[species],
# seqeuence_names[species]),
# out_file, format=sequence_file_format)
elif mode == "families":
if species_label_first:
label_sequence = lambda label, name: "%s%s%s" % (
label, separator_for_labeling, name)
else:
label_sequence = lambda label, name: "%s%s%s" % (
name, separator_for_labeling, label)
"""
def per_family_record_generator(seq_super_dict, clust_dict, cluster_id):
for species in seq_super_dict:
#print species, cluster_id
for record_id in clust_dict[species][cluster_id]:
if label_species:
record = deepcopy(seq_super_dict[species][record_id])
record.id = label_sequence(species, record_id)
yield record
else:
yield seq_super_dict[species][record_id]
"""
for cluster_name in cluster_names:
out_file = "%s%s.%s" % (out_dir, cluster_name,
sequence_file_extension)
with self.metaopen(out_file) as out_fd:
for species in sequence_super_dict:
sequence_super_dict[species].write(
out_fd,
keep_file_open=True,
whitelist=clusters_dict[species][cluster_name],
out_id_expression=label_sequence
if label_species else None)
(args.species_dir, species))
species_syn_dict.write("families_all_species.t", absent_symbol=".")
nonassembled = species_syn_dict.filter_by_line(filter_nonassembled)
species_syn_dict.write("correctly_assembled_families_species.t",
absent_symbol=".")
nonassembled.write("not_assembled_families_in_all_species.t",
absent_symbol=".")
complicated_families_dict = nonassembled.filter_by_line(
filter_splited_to_several_fam)
complicated_families_dict.write("complicated_families.t", absent_symbol=".")
complicated_families_syn_dict = SynDict()
complicated_families_syn_ids = IdSet()
sl_keys = list(complicated_families_dict.sl_keys())
for sl_key in sl_keys:
sp_set = set()
for species in complicated_families_dict:
if sl_key not in complicated_families_dict[species]:
continue
tmp = complicated_families_dict[species][sl_key].split(";")
for i in range(0, len(tmp)):
if "_" in tmp[i]:
tmp[i] = tmp[i][2:]
tmp[i] = tmp[i].split(",")
for syn_id in tmp[i]:
complicated_families_syn_ids.add(syn_id)
sp_set.add(syn_id)
complicated_families_syn_dict[sl_key] = sp_set
def prepare_template_for_popart(alignment_file,
output_file,
haplotype_fam_file=None,
traits_file=None,
whitelist_file=None):
from RouToolPa.Parsers.Sequence import CollectionSequence
sequence_collection = CollectionSequence(in_file=alignment_file,
parsing_mode="parse")
sequence_collection.get_stats_and_features(count_gaps=False,
sort=False)
whitelist = IdSet(filename=whitelist_file)
alignment_len = sequence_collection.seq_lengths["length"].unique()
if len(alignment_len) > 1:
raise ValueError(
"ERROR!!! Sequences in alignment have different lengths!")
alignment_len = alignment_len[0]
haplotype_selected_sequence_dict = SynDict()
haplotypes_without_sequences_ids = IdList()
traits_df = pd.read_csv(
traits_file, sep="\t",
index_col=0) if traits_file else pd.DataFrame()
if haplotype_fam_file:
haplotype_dict = SynDict(filename=haplotype_fam_file,
split_values=True)
for haplotype_id in haplotype_dict:
for sequence_id in haplotype_dict[haplotype_id]:
if sequence_id in sequence_collection.records:
haplotype_selected_sequence_dict[
haplotype_id] = sequence_id
break
else:
haplotypes_without_sequences_ids.append(haplotype_id)
else:
haplotype_dict = dict([(entry, [entry])
for entry in sequence_collection.scaffolds])
haplotype_selected_sequence_dict = dict([
(entry, entry) for entry in sequence_collection.scaffolds
])
final_haplotype_set = (set(haplotype_selected_sequence_dict.keys())
& whitelist) if whitelist else set(
haplotype_selected_sequence_dict.keys())
with open(output_file, "w") as out_fd:
#out_fd.write("#NEXUS\nBEGIN TAXA;\nDIMENSIONS\nNTAX = %i;\nTAXLABELS\n%s\n;\nEND;\n\n" % (len(haplotype_selected_sequence_dict),
# "\n".join(haplotype_selected_sequence_dict.keys())))
out_fd.write("#NEXUS\n\n")
out_fd.write(
"BEGIN DATA;\n\tDIMENSIONS NTAX=%i NCHAR=%i;\n\tFORMAT DATATYPE=DNA MISSING=? GAP=- MATCHCHAR=. ;\n"
% (len(final_haplotype_set), alignment_len))
out_fd.write("\tMATRIX\n")
for haplotype_id in final_haplotype_set:
out_fd.write(
"\t\t%s %s\n" % (haplotype_id, sequence_collection.records[
haplotype_selected_sequence_dict[haplotype_id]]))
out_fd.write("\t;\nEND;\n\n")
if not traits_df.empty:
traits_number = len(traits_df.columns)
out_fd.write(
"BEGIN TRAITS;\n\tDimensions NTRAITS={0};\n\tFormat labels=yes missing=? separator=Comma;\n"
.format(traits_number))
out_fd.write("\tTraitLabels {0};\n".format(" ".join(
traits_df.columns)))
out_fd.write("\tMATRIX\n")
for haplotype_id in final_haplotype_set:
out_fd.write(
"\t\t%s %s\n" %
(haplotype_id,
",".join(map(str, traits_df.loc[haplotype_id]))
if haplotype_id in traits_df.index else
("0," * traits_number)[:-1]))
else:
out_fd.write(
"BEGIN TRAITS;\n\tDimensions NTRAITS=1;\n\tFormat labels=yes missing=? separator=Comma;\n"
)
out_fd.write("\tTraitLabels Area;\n")
out_fd.write("\tMATRIX\n")
for haplotype_id in final_haplotype_set:
out_fd.write(
"\t\t%s %i\n" %
(haplotype_id, len(haplotype_dict[haplotype_id])))
out_fd.write("\t;\nEND;\n\n")
action="store",
dest="extension",
help="Extension of output files. Default: equal to -f")
parser.add_argument(
"-d",
"--id_file",
action="store",
dest="id_file",
help="File with groups of sequences to extract(.fam file).")
args = parser.parse_args()
FileRoutines.safe_mkdir(args.output)
args.extension = args.extension if args.extension else args.format
tmp_index_file = "temp.idx"
#id_list = read_ids(args.id_file)
id_list = IdSet(filename=args.id_file)
sequence_groups_id = SynDict()
sequence_groups_id.read(args.id_file, split_values=True)
#print("Parsing %s..." % args.input_file)
sequence_dict = SeqIO.index_db(tmp_index_file, args.input, format=args.format)
for group in sequence_groups_id:
SeqIO.write(SequenceRoutines.record_by_id_generator(
sequence_dict, sequence_groups_id[group], verbose=True),
"%s%s.%s" % (args.output, group, args.extension),
format=args.format)
os.remove(tmp_index_file)
help="Directory with families of species")
"""
parser.add_argument("-o", "--output_file", action="store", dest="output", default="stdout",
help="Output file. Default: stdout")
"""
args = parser.parse_args()
# run after scripts/expansion/compare_cluster.py
# out_fd = sys.stdout if args.output == "stdout" else open(args.output, "w")
species_syn_dict = TwoLvlDict()
for species in args.species_list:
species_syn_dict[species] = read_synonyms_dict("%s%s/all.t" % (args.species_dir, species))
species_syn_dict.write("families_all_species.t", absent_symbol=".")
not_assembled = species_syn_dict.filter_by_line(is_assembled)
species_syn_dict.write("correctly_assembled_families_species.t", absent_symbol=".")
assembled_ids = IdSet(species_syn_dict.sl_keys())
assembled_ids.write("assembled_families.ids")
not_assembled_ids = IdSet(not_assembled.sl_keys())
not_assembled_ids.write("non_assembled_families.ids")
"""
if args.output != "stdout":
out_fd.close()
"""
def compare_multiple_genome_results(self, busco_file_list, output_prefix, label_list=None,
black_scaffold_list=(), white_scaffold_list=()):
busco_table_dict = OrderedDict()
gene_id_dict = OrderedDict()
counts_dict = OrderedDict()
output_path_list = self.split_filename(output_prefix)
pairwise_overlaps_dir = "%s/pairwise_overlaps/" % (output_path_list[0] if output_path_list[0] else ".")
pairwise_overlap_counts_dir = "%s/pairwise_overlap_counts/" % (output_path_list[0] if output_path_list[0] else ".")
self.safe_mkdir(pairwise_overlaps_dir)
self.safe_mkdir(pairwise_overlap_counts_dir)
lllabels_list = label_list if label_list else ["A%i" % i for i in range(1, len(busco_file_list) + 1)]
for busco_table, label in zip(busco_file_list, lllabels_list):
busco_table_dict[label] = BUSCOtable(in_file=busco_table, black_list=black_scaffold_list,
white_list=white_scaffold_list)
gene_id_dict[label] = OrderedDict()
counts_dict[label] = OrderedDict()
gene_id_dict[label], counts_dict[label] = busco_table_dict[label].count_statuses()
# TODO: draw piecharts
# TODO: count overlaps
pairwise_overlap_dict = OrderedDict()
count_pairwise_overlap_dict = OrderedDict()
for label1 in lllabels_list:
for label2 in lllabels_list:
if label1 == label2:
continue
overlap_id = "%s_vs_%s" % (label1, label2)
pairwise_overlap_dict[overlap_id] = TwoLvlDict()
count_pairwise_overlap_dict[overlap_id] = TwoLvlDict()
for status1 in self.status_list:
pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)] = OrderedDict()
count_pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)] = OrderedDict()
for status2 in self.status_list:
pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)]["%s@%s" % (label2, status2)] = IdSet(gene_id_dict[label1][status1] & gene_id_dict[label2][status2])
count_pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)]["%s@%s" % (label2, status2)] = len(pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)]["%s@%s" % (label2, status2)])
pairwise_overlap_dict[overlap_id]["%s@%s" % (label1, status1)]["%s@%s" % (label2, status2)].write("%s/%s.%s_vs_%s.ids" % (pairwise_overlaps_dir, output_prefix, "%s@%s" % (label1, status1), "%s@%s" % (label2, status2)))
count_pairwise_overlap_dict[overlap_id].write("%s/%s.overlap.%s.tsv" % (pairwise_overlap_counts_dir, output_prefix, overlap_id))
if 2 <= len(busco_file_list) <= 3:
fig, subplot_list = plt.subplots(2, 2, figsize=(6, 6))
plt.suptitle("Overlaps for BUSCO categories between assemblies/genomes")
#print(subplot_list)
for status, index in zip(self.status_list, range(0, 4)):
plt.sca(subplot_list[index // 2][index % 2])
plt.title(status)
MatplotlibRoutines.venn_diagram_from_sets(gene_id_dict[lllabels_list[0]][status],
gene_id_dict[lllabels_list[1]][status],
set3=gene_id_dict[lllabels_list[2]][status] if len(lllabels_list) > 2 else None,
set_labels=lllabels_list, set_colors=["red", "yellow", "green"],
output_prefix=None, extensions=("png",), title=None)
plt.savefig("%s.venn.png" % output_prefix)
plt.close()