def _load_monomer_info(annotations_file, identities_file, target_sequence,
alignment_file, use_best_reciprocal,
identity_threshold):
# read in annotation to a file and rename the appropriate column
annotation_table = read_species_annotation_table(annotations_file)
# read identity file
similarities = pd.read_csv(identities_file)
# create a pd.DataFrame containing the best hit in each organism
most_similar_in_species = most_similar_by_organism(
similarities, annotation_table)
if use_best_reciprocal:
paralogs = find_paralogs(target_sequence, annotation_table,
similarities, identity_threshold)
most_similar_in_species = filter_best_reciprocal(
alignment_file, paralogs, most_similar_in_species)
return most_similar_in_species
def describe_concatenation(annotation_file_1, annotation_file_2,
genome_location_filename_1, genome_location_filename_2,
outfile):
"""
Describes properties of concatenated alignment.
Writes a csv with the following columns
num_seqs_1 : number of sequences in the first monomer alignment
num_seqs_2 : number of sequences in the second monomer alignment
num_nonred_species_1 : number of unique species annotations in the
first monomer alignment
num_nonred_species_2 : number of unique species annotations in the
second monomer alignment
num_species_overlap: number of unique species found in both alignments
median_num_per_species_1 : median number of paralogs per species in the
first monomer alignmment
median_num_per_species_2 : median number of paralogs per species in
the second monomer alignment
num_with_embl_cds_1 : number of IDs for which we found an EMBL CDS in the
first monomer alignment (relevant to distance concatention only)
num_with_embl_cds_2 : number of IDs for which we found an EMBL CDS in the
first monomer alignment (relevant to distance concatention only)
Parameters
----------
annotation_file_1 : str
Path to annotation.csv file for first monomer alignment
annotation_file_2 : str
Path to annotation.csv file for second monomer alignment
genome_location_filename_1 : str
Path to genome location mapping file for first alignment
genome_location_filename_2 : str
Path to genome location mapping file for second alignment
outfile: str
Path to output file
"""
# load the annotations for each alignment
# as a pd.DataFrame
annotations_1 = read_species_annotation_table(
annotation_file_1
)
species_1 = annotations_1.species.values
annotations_2 = read_species_annotation_table(
annotation_file_2
)
species_2 = annotations_2.species.values
# calculate the number of sequences found in each alignment
num_seqs_1 = len(annotations_1)
num_seqs_2 = len(annotations_2)
# calculate the number of species found in each alignment
# where a species is defined as a unique OS or Tax annotation field
nonredundant_annotations_1 = len(set(species_1))
nonredundant_annotations_2 = len(set(species_2))
# calculate the number of overlapping species
species_overlap = list(
set(species_1).intersection(set(species_2))
)
n_species_overlap = len(species_overlap)
# calculate the median number of paralogs per species
n_paralogs_1 = float(
# counts the number of times each species occurs in the list
# then takes the median
np.median(list(Counter(species_1).values()))
)
n_paralogs_2 = float(
np.median(list(Counter(species_2).values()))
)
# If the user provided genome location files, calculate the number
# of ids for which we found an embl CDS
if genome_location_filename_1 is not None and \
genome_location_filename_2 is not None:
genome_location_table_1 = pd.read_csv(genome_location_filename_1)
genome_location_table_2 = pd.read_csv(genome_location_filename_2)
# Number uniprot IDs with EMBL CDS that is not NA
embl_cds1 = len(list(set(genome_location_table_1.uniprot_ac)))
embl_cds2 = len(list(set(genome_location_table_2.uniprot_ac)))
else:
embl_cds1 = np.nan
embl_cds2 = np.nan
concatenation_data = [
num_seqs_1,
num_seqs_2,
nonredundant_annotations_1,
nonredundant_annotations_2,
n_species_overlap,
n_paralogs_1,
n_paralogs_2,
embl_cds1,
embl_cds2,
]
cols = [
"num_seqs_1",
"num_seqs_2",
"num_nonred_species_1",
"num_nonred_species_2",
"num_species_overlap",
"median_num_per_species_1",
"median_num_per_species_2",
"num_with_embl_cds_1",
"num_with_embl_cds_2",
]
# create dataframe and store
data_df = pd.DataFrame(
[concatenation_data], columns=cols
)
data_df.to_csv(outfile)