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group_segments.py
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group_segments.py
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"""Script to group segments together into sequence entries prior to submission to Loculus
Example output for a single isolate with 3 segments:
"KJ682796.1.L/KJ682809.1.M/KJ682819.1.S": {
"ncbiReleaseDate": "2014-07-06T00:00:00Z",
"ncbiSourceDb": "GenBank",
"authors": "D. Goedhals, F.J. Burt, J.T. Bester, R. Swanepoel",
"insdcVersion_L": "1",
"insdcVersion_M": "1",
"insdcVersion_S": "1",
"insdcAccessionFull_L": "KJ682796.1",
"insdcAccessionFull_M": "KJ682809.1",
"insdcAccessionFull_S": "KJ682819.1",
"hash_L": "ddbfc33d45267e9c1a08f8f5e76d3e39",
"hash_M": "f64777883ba9f5293257698255767f2c",
"hash_S": "f716ed13dca9c8a033d46da2f3dc2ff1",
"hash": "ce7056d0bd7e3d6d3eca38f56b9d10f8",
"submissionId": "KJ682796.1.L/KJ682809.1.M/KJ682819.1.S"
},"""
import hashlib
import json
import logging
import pathlib
from collections import defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Final
import click
import orjsonl
import yaml
logger = logging.getLogger(__name__)
logging.basicConfig(
encoding="utf-8",
level=logging.DEBUG,
format="%(asctime)s %(levelname)8s (%(filename)20s:%(lineno)4d) - %(message)s ",
datefmt="%H:%M:%S",
)
@dataclass(frozen=True)
class Config:
compound_country_field: str
fasta_id_field: str
insdc_segment_specific_fields: list[str] # What does this field mean?
nucleotide_sequences: list[str]
segmented: bool
SPECIAL_FIELDS: Final = {"segment", "submissionId"}
@click.command()
@click.option("--config-file", required=True, type=click.Path(exists=True))
@click.option("--input-seq", required=True, type=click.Path(exists=True))
@click.option("--input-metadata", required=True, type=click.Path(exists=True))
@click.option("--output-seq", required=True, type=click.Path())
@click.option("--output-metadata", required=True, type=click.Path())
@click.option(
"--log-level",
default="INFO",
type=click.Choice(["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]),
)
def main(
config_file: str,
input_seq: str,
input_metadata: str,
output_seq: str,
output_metadata: str,
log_level: str,
) -> None:
logger.setLevel(log_level)
logging.getLogger("requests").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
full_config = yaml.safe_load(pathlib.Path(config_file).read_text(encoding="utf-8"))
relevant_config = {key: full_config[key] for key in Config.__annotations__}
config = Config(**relevant_config)
logger.info(config)
if not config.segmented:
raise ValueError({"ERROR: You are running a function that requires segmented data"})
logger.info(f"Reading metadata from {input_metadata}")
segments = config.nucleotide_sequences
number_of_segments = len(segments)
with open(input_metadata, encoding="utf-8") as file:
segment_metadata: dict[str, dict[str, str]] = json.load(file)
number_of_segmented_records = len(segment_metadata.keys())
logger.info(f"Found {number_of_segmented_records} individual segments in metadata file")
# Group segments according to isolate, collection date and isolate specific values
# These are the fields that are expected to be identical across all segments for a given isolate
first_row = next(iter(segment_metadata.values()))
if not first_row:
msg = "No data found in metadata file"
raise ValueError(msg)
all_fields = first_row.keys()
insdc_segment_specific_fields = set(config.insdc_segment_specific_fields)
insdc_segment_specific_fields.add("hash")
shared_fields = set(all_fields) - insdc_segment_specific_fields - SPECIAL_FIELDS
# Build equivalence classes based on shared fields
# Use shared fields as the key to group the data
type SegmentName = str
type Accession = str
type EquivalenceClasses = dict[tuple[str, str], dict[SegmentName, list[Accession]]]
# Creating the nested defaultdict with type hints
equivalence_classes: EquivalenceClasses = defaultdict(lambda: defaultdict(list))
for accession, values in segment_metadata.items():
group_key = tuple((field, values[field]) for field in shared_fields if values[field])
segment = values["segment"]
equivalence_classes[group_key][segment].append(accession)
# TODO: Advanced checks for various sub-classes so we can warn the user if there are issues
# For example, if there are multiple isolates with the same name and collection date
# We are being very strict here, but this is a good thing as it will catch errors
# We can always merge the data later if we need to
grouped_accessions: list[dict[SegmentName, Accession]] = []
# Simply check there are no duplicate segments for each group
for group_key, sequence_group in equivalence_classes.items():
# Verify that all segments are unique for the group
unique_per_segment = all(len(accessions) <= 1 for accessions in sequence_group.values())
if not unique_per_segment:
logger.warning(
f"Found multiple copies of a segment for grouping key: {group_key} "
"uploading segments individually. "
f"Grouping: {dict(sequence_group)}"
)
for segment, accessions in sequence_group.items():
grouped_accessions.extend([{segment: accession} for accession in accessions])
continue
# If all segments are unique, we can group them together
# We know that all segments are unique, so we can just unnest the list
grouped_accessions.append(
{segment: accessions[0] for segment, accessions in sequence_group.items()}
)
number_of_groups = len(grouped_accessions)
group_lower_bound = number_of_segmented_records // number_of_segments
group_upper_bound = number_of_segmented_records
logging.info(f"Total of {number_of_groups} groups left after merging")
if number_of_groups < group_lower_bound:
raise ValueError(
{
"There are too few groups after merging, indicating a problem with the data. "
f"Expected at least {group_lower_bound} groups (all merged), "
f"but found {number_of_groups}"
}
)
if number_of_groups > group_upper_bound:
raise ValueError(
{
"There are too many groups after merging, indicating a problem with the data. "
f"Expected at most {group_upper_bound} groups (all separate), "
f"but found {number_of_groups}"
}
)
# Add segment specific metadata for the segments
metadata: dict[str, dict[str, str]] = {}
# Map from original accession to the new concatenated accession
fasta_id_map: dict[Accession, Accession] = {}
for group in grouped_accessions:
# Create key by concatenating all accession numbers with their segments
# e.g. AF1234_S/AF1235_M/AF1236_L
# Sort the segments per config.nucleotide_sequences
row = {}
joint_key = "/".join(
[
f"{group[segment]}.{segment}"
for segment in config.nucleotide_sequences
if segment in group
]
)
for segment, accession in group.items():
fasta_id_map[accession] = f"{joint_key}_{segment}"
for field in shared_fields:
values = {segment: segment_metadata[group[segment]][field] for segment in group}
deduplicated_values = set(values.values())
if len(deduplicated_values) != 1:
msg = f"Assertion failed: values for group must be identical: {values}"
raise ValueError(msg)
row[field] = deduplicated_values.pop()
for field in insdc_segment_specific_fields:
for segment in config.nucleotide_sequences:
row[f"{field}_{segment}"] = (
segment_metadata[group[segment]][field] if segment in group else ""
)
row["submissionId"] = joint_key
row["hash"] = hashlib.md5(
json.dumps(row, sort_keys=True).encode(), usedforsecurity=False
).hexdigest()
metadata[joint_key] = row
Path(output_metadata).write_text(json.dumps(metadata, indent=4), encoding="utf-8")
logging.info(f"Wrote grouped metadata for {len(metadata)} sequences")
count = 0
count_ignored = 0
for record in orjsonl.stream(input_seq):
accession = record["id"]
raw_sequence = record["sequence"]
if accession not in fasta_id_map:
logger.warning(f"Accession {accession} not found in input sequence file, skipping")
count_ignored += 1
continue
orjsonl.append(
output_seq,
{
"id": fasta_id_map[accession],
"sequence": raw_sequence,
},
)
count += 1
logging.info(f"Wrote {count} sequences")
logging.info(f"Ignored {count_ignored} sequences as not found in {input_seq}")
if __name__ == "__main__":
main()