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SequenceParsingUtil.py
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SequenceParsingUtil.py
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"""
=============================================================================
This script contains the utility functions for preprocessing host information
and identifying annotated receptor-binding proteins
@author Mark Edward M. Gonzales
=============================================================================
"""
import os
from collections import defaultdict
import nltk
import numpy as np
import regex as re
from Bio import SeqIO
class SequenceParsingUtil(object):
def __init__(
self, display_progress=None, misspelling_threshold=None, min_len_keyword=None
):
"""
Constructor
Parameters:
- display_progress: True if the number of entries processed is to be displayed periodically; False, otherwise
- misspelling_threshold: Minimum edit distance to be considered a possible misspelling
- min_len_keyword: Minimum length for a token to be considered a keyword of interest
"""
self.display_progress = display_progress
self.misspelling_threshold = misspelling_threshold
self.min_len_keyword = min_len_keyword
self.token_delimiter = None
self.inphared_gb = None
self.inphared = None
self.phages_unspec_host = None
self.unfiltered_hosts = None
self.genus_typo = None
self.unfiltered_suspected_genera = None
self.excluded_hosts = None
self.valid_hosts = None
self.hypothetical_keywords = set()
self.rbp_related_not_rbp = set()
self.putative_functions = set()
self.no_cds_annot = None
self.annot_products = None
self.rbp_products = None
self.hypothetical_proteins = None
def __display_progress(self, ctr):
"""
Periodically displays the number of processed entries
Parameters:
- ctr: Number of processed entries
"""
if ctr % self.display_progress == 0:
print("Processed", ctr, "records")
# =======
# Setters
# =======
def set_inphared_gb(self, inphared_gb):
"""
Sets the consolidated GenBank entries of the entries fetched via INPHARED
Parameters:
- inphared_gb: File path of the consolidated GenBank entries of the entries fetched via INPHARED
"""
self.inphared_gb = inphared_gb
def set_inphared(self, inphared):
"""
Sets the dataset (phage-host table, along with other information)
Parameters:
- inphared: File path of the dataset (phage-host table, along with other information)
"""
self.inphared = inphared
def set_no_cds_annot(self, no_cds_annot):
"""
Sets the phage entries without coding sequence information
Parameters:
- no_cds_annot: Set of phage entries without coding sequence information
"""
self.no_cds_annot = no_cds_annot
def set_annot_products(self, annot_products):
"""
Sets the gene product annotations
Parameters:
- annot_products: Set of gene product annotations
"""
self.annot_products = annot_products
def set_rbp_products(self, rbp_products):
"""
Sets the RBP annotations
Parameters:
- rbp_products: Set of RBP annotations
"""
self.rbp_products = rbp_products
def set_hypothetical_proteins(self, hypothetical_proteins):
"""
Sets the hypothetical protein annotations
Parameters:
- hypothetical_proteins: Set of hypothetical protein annotations
"""
self.hypothetical_proteins = hypothetical_proteins
def set_token_delimiter(self, token_delimiter):
"""
Sets the possible token delimiters for gene product annotations
Parameters:
- token_delimiters: Regular expression for the possible token delimiters for gene product annotations
"""
self.token_delimiter = token_delimiter
# ==================
# Data Preprocessing
# ==================
def get_phages_unspec_host(self, inphared_unspec_host):
"""
Retrieves the isolation host information for phages where the returned host information of INPHARED is "unspecified"
Parameters:
- inphared_unspec_host: DataFrame containing the phage entries with unspecified host information
Returns:
- Dictionary where each key is a phage with unspecified host information and the value is the list of isolation
hosts retrieved from GenBank
"""
records = SeqIO.parse(self.inphared_gb, "gb")
accession_np = inphared_unspec_host["Accession"].to_numpy()
phages_unspec_host = defaultdict(list)
ctr = 0
iter_flag = True
while iter_flag:
try:
record = next(records)
try:
idx = np.where(accession_np == record.name)[0][0]
if "host" in record.features[0].qualifiers:
phages_unspec_host[record.name].append(
record.features[0].qualifiers["host"]
)
else:
phages_unspec_host[record.name].append([])
if "lab_host" in record.features[0].qualifiers:
phages_unspec_host[record.name].append(
record.features[0].qualifiers["lab_host"]
)
else:
phages_unspec_host[record.name].append([])
except IndexError:
pass
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
self.phages_unspec_host = phages_unspec_host
return phages_unspec_host
def get_unfiltered_hosts(self):
"""
Retrieves the isolation hosts of phages where the returned host information of INPHARED is "unspecified"
Returns:
- Set of isolation hosts of phages where the returned host information of INPHARED is "unspecified"
"""
unfiltered_hosts = set()
for key, value in self.phages_unspec_host.items():
for host in value[0]:
unfiltered_hosts.add(host)
for host in value[1]:
unfiltered_hosts.add(host)
self.unfiltered_hosts = unfiltered_hosts
return unfiltered_hosts
def get_genus_typo(self, filename):
"""
Retrieves the genera with typographical errors
Parameters:
- filename: File path of the text file recording the genera with typographical errors
Returns:
- Dictionary where each key is the misspelled genus and the value is the correct spelling
"""
genus_typo = {}
with open(filename) as typo_file:
for entry in typo_file:
typo, correct = entry.split("\t")
typo = typo.rstrip("\n")
correct = correct.rstrip("\n")
genus_typo[typo] = correct
self.genus_typo = genus_typo
return genus_typo
def __get_suspected_genus(self, candidate_regex, host):
"""
Returns the genus of the isolation host of a phage where the returned host information of INPHARED is "unspecified"
Parameters:
- candidate_regex: Regex for matching candidate genera
Returns:
- Genus of the isolation host of a phage where the returned host information of INPHARED is "unspecified"
"""
host_tokens = host.split(" ")
if re.search(candidate_regex, host_tokens[0], re.IGNORECASE):
# Handle the case where name starts with "Candidate division"
if host_tokens[1] == "division":
genus = host_tokens[2].lower()
else:
genus = host_tokens[1].lower()
else:
genus = host_tokens[0].lower()
if genus in self.genus_typo:
genus = self.genus_typo[genus]
return genus
def get_unfiltered_suspected_genera(self, candidate_regex):
"""
Returns the genera of the isolation hosts of phages where the returned host information of INPHARED is "unspecified"
Parameters:
- candidate_regex: Regex for matching candidate genera
Returns:
- Set of genera of the isolation hosts of phages where the returned host information of INPHARED is "unspecified"
"""
unfiltered_suspected_genera = set()
for unfiltered_host in self.unfiltered_hosts:
unfiltered_suspected_genera.add(
self.__get_suspected_genus(candidate_regex, unfiltered_host)
)
self.unfiltered_suspected_genera = unfiltered_suspected_genera
return unfiltered_suspected_genera
def get_excluded_hosts(self, *exclusion_files):
"""
Retrieves the isolation hosts that do not pertain to bacterial hosts or those that are less specific than genus level
Parameters:
- exclusion files: File path(s) of the text file(s) recording the isolation hosts in GenBank that do not pertain
to bacterial hosts or those that are less specific than genus level
Returns:
- Set of isolation hosts that do not pertain to bacterial hosts or those that are less specific than genus level
"""
excluded_hosts = set()
for exclusion_file in exclusion_files:
with open(exclusion_file, "r") as file:
for host in file:
excluded_hosts.add(host.rstrip("\n"))
self.excluded_hosts = excluded_hosts
return excluded_hosts
def get_valid_hosts(self):
"""
Returns the isolation hosts that pertain to bacterial hosts at the genus level
Returns:
- Set of isolation hosts that pertain to bacterial hosts at the genus level
"""
self.valid_hosts = self.unfiltered_suspected_genera - self.excluded_hosts
return self.valid_hosts
def is_possible_misspelling(self, typo, keyword):
"""
Returns True if a word is a possible misspelling; False, otherwise. This function uses the minimum edit distance
as a heuristic for identifying possible misspellings
Parameters:
- typo: Potentially misspelled word
- keyword: Correct spelling
Returns:
- True if a word is a possible misspelling; False, otherwise. This function uses the minimum edit distance
as a heuristic for identifying possible misspellings
"""
return (
len(keyword) >= self.min_len_keyword
and nltk.edit_distance(typo, keyword, transpositions=True)
<= self.misspelling_threshold
)
def update_host_column(
self, candidate_regex, inphared_unspec_host, inphared_augmented
):
"""
Updates the host column of the phage-host table with the isolation host information from GenBank
Parameters:
- candidate_regex: Regex for matching candidate genera
- inphared_unspec_host: DataFrame containing the phage entries with unspecified host information
- inphared_augmented: Original phage-host table to be updated
"""
records = SeqIO.parse(self.inphared_gb, "gb")
accession_unspec_np = inphared_unspec_host["Accession"].to_numpy()
accession_augmented_np = inphared_augmented["Accession"].to_numpy()
ctr = 0
iter_flag = True
while iter_flag:
try:
record = next(records)
try:
unspec_idx = np.where(accession_unspec_np == record.name)[0][0]
hosts = set()
if "host" in record.features[0].qualifiers:
for host in record.features[0].qualifiers["host"]:
host_genus = self.__get_suspected_genus(
candidate_regex, host
)
if host_genus in self.valid_hosts:
hosts.add(host_genus)
if "lab_host" in record.features[0].qualifiers:
for host in record.features[0].qualifiers["lab_host"]:
host_genus = self.__get_suspected_genus(
candidate_regex, host
)
if host_genus in self.valid_hosts:
hosts.add(host_genus)
if hosts:
hosts_str = " | ".join(hosts)
augmented_idx = np.where(accession_augmented_np == record.name)[
0
][0]
inphared_augmented.at[augmented_idx, "Host"] = hosts_str
print(
"Index:",
augmented_idx,
"| Name:",
record.name,
"| Host:",
hosts_str,
)
except IndexError:
pass
# Display progress
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
def get_ncbi_standard_nomenclature(self, filename):
"""
Retrieves the standard nomenclature following NCBI Taxonomy
Parameters:
- filename: File path of the text file containing the equivalent nomenclature of a genus following NCBI Taxonomy
"""
nomenclature = {}
with open(filename) as nomenclature_file:
for entry in nomenclature_file:
nonstandard, standard = entry.split("\t")
nonstandard = nonstandard.rstrip("\n")
standard = standard.rstrip("\n")
nomenclature[nonstandard] = standard
return nomenclature
# ===============
# Gene annotation
# ===============
def get_no_cds_annot(self):
"""
Returns the set of phage entries without gene annotation
Returns:
- Set of phage entries without gene annotation
"""
records = SeqIO.parse(self.inphared_gb, "gb")
ctr = 0
iter_flag = True
no_cds_annot = set()
while iter_flag:
try:
record = next(records)
try:
idx = np.where(self.inphared == record.name)[0][0]
has_cds_annot = False
for feature in record.features:
if feature.type == "CDS":
has_cds_annot = True
break
if not has_cds_annot:
no_cds_annot.add((idx, record.name))
except IndexError:
pass
# Display progress
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
self.no_cds_annot = no_cds_annot
return no_cds_annot
# ====================================
# Process entries with gene annotation
# ====================================
def construct_keyword_list(
self, hypothetical_file, rbp_related_file, putative_functions_file
):
"""
Constructs a list of keywords associated with hypothetical proteins, proteins with putative functions, and RBP-related
proteins that are not RBPs themselves
Parameters:
- hypothetical_file: File path of the text file containing keywords associated with hypothetical proteins
- rbp_related_file: File path of the text file containing keywords associated with RBP-related proteins
that are not RBPs themselves
- putative_functions_file: File path of the text file containing keywords associated with proteins with
putative functions
"""
with open(hypothetical_file) as file:
for keyword in file:
self.hypothetical_keywords.add(keyword.rstrip("\n"))
with open(rbp_related_file) as file:
for keyword in file:
self.rbp_related_not_rbp.add(keyword.rstrip("\n"))
with open(putative_functions_file) as file:
for keyword in file:
self.putative_functions.add(keyword.rstrip("\n"))
self.putative_functions = self.putative_functions.union(
self.rbp_related_not_rbp
)
def get_annot_products(self):
"""
Retrieves the annotations for the gene products in GenBank
Returns:
- Set of annotations for the gene products in GenBank
"""
records = SeqIO.parse(self.inphared_gb, "gb")
ctr = 0
iter_flag = True
annot_products = set()
while iter_flag:
try:
record = next(records)
try:
idx = np.where(self.inphared == record.name)[0][0]
for feature in record.features:
if feature.type == "CDS":
try:
annot_products.add(feature.qualifiers["product"][0])
except KeyError:
pass
except IndexError:
pass
# Display progress
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
self.annot_products = annot_products
return annot_products
def __is_rbp_related_but_not_rbp(self, product):
"""
Checks if a given annotation pertains to an RBP-related protein that is not an RBP itself
Parameters:
- product: Annotation for a gene product
Returns:
- True if a given annotation pertains to an RBP-related protein that is not an RBP itself; False, otherwise
"""
for keyword in self.rbp_related_not_rbp:
if keyword in product:
return True
else:
for token in re.split(self.token_delimiter, product):
# 'unamed' and 'named' are within the minimum edit distance threshold for misspellings if token is 'rapid'
if token != "rapid" and self.is_possible_misspelling(
token, keyword
):
return True
return False
def __has_putative_function(self, product):
"""
Checks if a given annotation pertains to a protein with a putative function
Parameters:
- product: Annotation for a gene product
Returns:
- True if a given annotation pertains to a protein with a putative function; False, otherwise
"""
for keyword in self.putative_functions:
if keyword in product:
return True
else:
for token in re.split(self.token_delimiter, product):
# 'unamed' and 'named' are within the minimum edit distance threshold for misspellings if token is 'rapid'
if token != "rapid" and self.is_possible_misspelling(
token, keyword
):
return True
# Enzymes usually end with '-ase'
if "ase" in token:
return True
return False
def get_rbp_hypothetical_proteins(self, rbp_regex):
"""
Construct a list of annotations for RBPs and hypothetical proteins from GenBank annotations
Parameters:
- rbp_regex: Regex for selecting annotated RBPs
Returns:
- List of annotations for RBPs from GenBank annotations
- List of annotations for hypothetical proteins from GenBank annotations
"""
rbp_products = set()
hypothetical_proteins = set()
ctr = 0
for annot_product in self.annot_products:
annot_product_lower = annot_product.lower()
if not self.__is_rbp_related_but_not_rbp(annot_product_lower) and re.search(
rbp_regex, annot_product_lower, re.IGNORECASE
):
rbp_products.add(annot_product_lower)
else:
hypothetical_keyword_found = False
if not self.__has_putative_function(annot_product_lower):
for keyword in self.hypothetical_keywords:
if not hypothetical_keyword_found:
if keyword in annot_product_lower:
hypothetical_proteins.add(annot_product_lower)
hypothetical_keyword_found = True
else:
tokens = re.split(
self.token_delimiter, annot_product_lower
)
for token in tokens:
# 'unamed' and 'named' are within the minimum edit distance threshold for misspellings
if (
token != "named"
and self.is_possible_misspelling(token, keyword)
):
hypothetical_proteins.add(annot_product_lower)
hypothetical_keyword_found = True
break
else:
break
ctr += 1
self.__display_progress(ctr)
self.rbp_products = rbp_products
self.hypothetical_proteins = hypothetical_proteins
return rbp_products, hypothetical_proteins
# ===================================
# Analyze distribution of RBP lengths
# ===================================
def __has_unknown_amino_acid(self, sequence):
"""
Checks if a given protein sequence has an undetermined or unrecognized amino acid
Parameters:
- sequence: Protein sequence to be checked
Returns:
- True if the protein sequence has an undetermined or unrecognized amino acid; False, otherwise
"""
unknown_aa_expr = r"[^ACDEFGHIKLMNPQRSTVWY]"
if re.search(unknown_aa_expr, sequence):
return True
return False
def __is_acceptable_length(self, sequence, lower_bound, upper_bound):
"""
Checks if a given protein sequence does not have an outlying length
Parameters:
- sequence: Protein sequence to be checked
- lower_bound: Lower bound of the lengths of RBPs (excluding outliers)
- upper_bound: Upper bound of the lengths of RBPs (excluding outliers)
Returns:
- True if the protein sequence does not have an outlying length; False, otherwise
"""
return lower_bound <= len(sequence) and len(sequence) <= upper_bound
def __is_rbp(self, product, sequence, lower_bound=-1, upper_bound=-1):
"""
Checks if a given gene product is an RBP
Parameters:
- product: Annotation for the gene product
- sequence: Protein sequence of the gene product
- lower_bound: Lower bound of the lengths of RBPs (excluding outliers)
- upper_bound: Upper bound of the lengths of RBPs (excluding outliers)
Returns:
- True if the gene product is an RBP; False, otherwise
"""
if lower_bound == -1 and upper_bound == -1:
return product in self.rbp_products and not self.__has_unknown_amino_acid(
sequence
)
return (
product in self.rbp_products
and not self.__has_unknown_amino_acid(sequence)
and self.__is_acceptable_length(sequence, lower_bound, upper_bound)
)
def generate_rbp_len_distribution(self):
"""
Returns a dictionary containing the counts of the lengths (number of amino acids) of the RBPs based on GenBank annotation
Returns:
- Dictionary where each key is an RBP length and the value is its count (i.e., the number of RBPs with that length)
"""
records = SeqIO.parse(self.inphared_gb, "gb")
ctr = 0
iter_flag = True
len_distribution = defaultdict(lambda: 0)
while iter_flag:
try:
record = next(records)
try:
idx = np.where(self.inphared == record.name)[0][0]
for feature in record.features:
if feature.type == "CDS":
try:
product = feature.qualifiers["product"][0].lower()
translation = feature.qualifiers["translation"][0]
if self.__is_rbp(product, translation):
len_distribution[len(translation)] += 1
except KeyError:
pass
except IndexError:
pass
# Display progress
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
return len_distribution
def generate_rbp_len_distribution_prokka(
self, len_distribution, complete_genome_dir
):
"""
Updates the dictionary containing the counts of the lengths of the RBPs with the lengths of the RBPs annotated
using Prokka
Parameters:
- len_distribution: Dictionary containing the counts of the lengths of the RBPs based on GenBank annotation
- complete_genome_dir: File path of the directory with the genomes of all the phage entries retrieved via INPHARED
"""
ctr = 0
for phage in self.no_cds_annot:
with open(f"{complete_genome_dir}/{phage[1]}/{phage[1]}.faa") as handle:
for record in SeqIO.parse(handle, "fasta"):
product = record.description.split(" ", 1)[1]
translation = str(record.seq)
if self.__is_rbp(product, translation):
len_distribution[len(translation)] += 1
# Display progress
ctr += 1
self.__display_progress(ctr)
# ===============================================
# Generate FASTA for entries with gene annotation
# ===============================================
def __is_hypothetical_protein(self, product, sequence, lower_bound, upper_bound):
"""
Checks if a given gene product is a hypothetical protein with length within the bounds for RBP lengths
Parameters:
- product: Annotation for the gene product
- sequence: Protein sequence of the gene product
- lower_bound: Lower bound of the lengths of RBPs (excluding outliers)
- upper_bound: Upper bound of the lengths of RBPs (excluding outliers)
Returns:
- True if the gene product is a hypothetical protein with length less within the bounds for RBP lengths
"""
return (
product in self.hypothetical_proteins
and not self.__has_unknown_amino_acid(sequence)
and self.__is_acceptable_length(sequence, lower_bound, upper_bound)
)
def generate_rbp_hypothetical_fasta(
self, rbp_genbank_dir, hypothetical_genbank_dir, lower_bound, upper_bound
):
"""
Generates the FASTA files containing the proteomes of the phages with coding sequence information in GenBank
Parameters:
- rbp_genbank_dir: File path of the directory with the RBP protein sequences of the phages with coding sequence
information in GenBank
- hypothetical_genbank_dir: File path of the directory with the hypothetical protein sequences of the phages
with coding sequence information in GenBank
- lower_bound: Lower bound of the lengths of RBPs (excluding outliers)
- upper_bound: Upper bound of the lengths of RBPs (excluding outliers)
"""
records = SeqIO.parse(self.inphared_gb, "gb")
ctr = 0
iter_flag = True
while iter_flag:
try:
record = next(records)
try:
idx = np.where(self.inphared == record.name)[0][0]
name = record.name
rbp_fasta_str = ""
hypothetical_fasta_str = ""
for feature in record.features:
if feature.type == "CDS":
try:
product = feature.qualifiers["product"][0].lower()
translation = feature.qualifiers["translation"][0]
if self.__is_rbp(
product, translation, lower_bound, upper_bound
):
protein_id = feature.qualifiers["protein_id"][0]
rbp_fasta_str += (
f">{protein_id} {product} \n{translation}\n"
)
elif self.__is_hypothetical_protein(
product, translation, lower_bound, upper_bound
):
protein_id = feature.qualifiers["protein_id"][0]
hypothetical_fasta_str += (
f">{protein_id} {product} \n{translation}\n"
)
except KeyError:
pass
if len(rbp_fasta_str) > 0:
file_name = f"{name}-rbp.fasta"
with open(
os.path.join(rbp_genbank_dir, file_name), "w"
) as rbp_fasta_file:
rbp_fasta_file.write(rbp_fasta_str)
if len(hypothetical_fasta_str) > 0:
file_name = f"{name}-hypothetical.fasta"
with open(
os.path.join(hypothetical_genbank_dir, file_name), "w"
) as hypothetical_fasta_file:
hypothetical_fasta_file.write(hypothetical_fasta_str)
except IndexError:
pass
# Display progress
ctr += 1
self.__display_progress(ctr)
except StopIteration:
iter_flag = False
def check_fasta_embeddings_per_phage(self, suffix, fasta_dir, embed_dir):
"""
Returns the difference between the phages in the embeddings and in the FASTA directories.
There should be a one-to-one correspondence between the phages in the embeddings and in the FASTA directories
Parameters:
- suffix: 'hypothetical' for hypothetical proteins or 'rbp' for RBPs
- fasta_dir: File path of the directory containing the FASTA files with the RBP sequences
- embed_dir: File path of the directory containing the embeddings of the RBP sequences
Returns:
- Set of phages in the embeddings directory but not in the FASTA directory
- Set of phages in the FASTA directory but not in the embeddings directory
"""
fasta = set()
for file in os.listdir(fasta_dir):
fasta.add(file[: -(1 + len(suffix) + len(".fasta"))])
embeddings = set()
for file in os.listdir(embed_dir):
embeddings.add(file[: -(1 + len(suffix) + len("-embeddings.csv"))])
return embeddings - fasta, fasta - embeddings
def check_fasta_embeddings_per_protein(self, suffix, fasta_dir, embed_dir):
"""
Prints the difference between the protein IDs in the embeddings and in the FASTA directories.
There should be a one-to-one correspondence between the protein IDs in the embeddings and in the FASTA directories
Parameters:
- suffix: 'hypothetical' for hypothetical proteins or 'rbp' for RBPs
- fasta_dir: File path of the directory containing the FASTA files with the RBP sequences
- embed_dir: File path of the directory containing the embeddings of the RBP sequences
Returns:
- List of phages where there are differences in the protein IDs in the embeddings and in the FASTA directories
"""
erroneous = []
ctr = 0
for file in os.listdir(fasta_dir):
with open(f"{fasta_dir}/{file}", "r") as fasta:
proteins_fasta, num_proteins_fasta = self.__get_proteins_fasta(fasta)
phage_name = file[: -(1 + len(suffix) + len(".fasta"))]
with open(
f"{embed_dir}/{phage_name}-{suffix}-embeddings.csv", "r"
) as embeddings:
proteins_embed, num_proteins_embed = self.get_proteins_embeddings(
embeddings
)
if (
proteins_fasta != proteins_embed
or num_proteins_fasta != num_proteins_embed
):
print(
phage_name,
"|",
proteins_fasta - proteins_embed,
"|",
proteins_embed - proteins_fasta,
"|",
num_proteins_fasta,
"|",
num_proteins_embed,
)
erroneous.append(phage_name)
# Display progress
ctr += 1
self.__display_progress(ctr)
return erroneous
def __get_proteins_fasta(self, fasta):
"""
Returns the protein IDs and the number of proteins in a file containing the proteome of a phage
Parameters:
- fasta: Contents of the file containing the proteome of a phage
Returns:
- Set of protein IDs in the file containing the proteome of a phage
- Number of proteins in the file containing the proteome of a phage
"""
proteins = set()
num_lines = 0
is_comment = True
for line in fasta:
if is_comment:
protein_name = line.split(" ")[0][1:]
proteins.add(protein_name)
num_lines += 1
is_comment = not is_comment
return proteins, num_lines
def get_proteins_embeddings(self, embeddings):
"""
Returns the protein IDs and the number of proteins in a file containing the embeddings of the protein sequences
of a phage
Parameters:
- embeddings: Contents of the file containing the embeddings of the protein sequences of a phage
Returns:
- Set of protein IDs in the file containing the embeddings of the protein sequences of a phage
- Number of proteins in the file containing the embeddings of the protein sequences of a phage
"""
proteins = set()
# Ignore header row
num_lines = -1
for line in embeddings:
protein_name = line.split(",")[0]
proteins.add(protein_name)
num_lines += 1
proteins.remove("ID")
return proteins, num_lines
# =======================================
# Process entries without gene annotation
# =======================================