EC2 Default User
commited on
Commit
•
de6ec9e
1
Parent(s):
a024f41
Try LFS storage
Browse files- .gitattributes +3 -0
- README.md +7 -0
- mouse_BALB_c.tar.gz +3 -0
- mouse_C57BL_6.tar.gz +3 -0
- oas-data-cleaning.py +80 -0
- oas-paired-sequence-data.py +0 -138
- rat_SD.tar.gz +3 -0
.gitattributes
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@@ -54,3 +54,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*/*.csv filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*/*.csv filter=lfs diff=lfs merge=lfs -text
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mouse_BALB_c.tar.gz filter=lfs diff=lfs merge=lfs -text
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mouse_C57BL_6.tar.gz filter=lfs diff=lfs merge=lfs -text
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rat_SD.tar.gz filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -4,6 +4,13 @@ language: en
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task_categories:
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- fill-mask
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license: cc-by-4.0
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---
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# Dataset Card for OAS Paired Sequence Data
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task_categories:
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- fill-mask
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license: cc-by-4.0
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configs:
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- config_name: rat_SD
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data_files: "rat_SD.tar.gz"
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- config_name: mouse_BALB_c
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data_files: "mouse_BALB_c.tar.gz"
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- config_name: mouse_C57BL_6
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data_files: "mouse_C57BL_6.tar.gz"
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---
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# Dataset Card for OAS Paired Sequence Data
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mouse_BALB_c.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:876fdae8478963175654c9d9ed7a8d97847e002b92c3112ee730eddff24118cf
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size 114634
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mouse_C57BL_6.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:d87e773f30d1ee949d46b2042603b2a060223db87be6309ee22aedbbab227290
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size 130209
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oas-data-cleaning.py
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# import boto3
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# import io
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import os
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import pandas as pd
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import re
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import shutil
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import tarfile
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data_dir = os.path.join(os.getcwd(), "data_units")
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# output_path = os.getcwd()
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# species_list = ["rat_SD", "mouse_BALB_c", "mouse_C57BL_6", "human"]
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species_list = ["rat_SD", "mouse_BALB_c", "mouse_C57BL_6"]
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# S3_BUCKET = "aws-hcls-ml"
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# S3_SRC_PREFIX = "oas-paired-sequence-data/raw"
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# S3_DEST_PREFIX = "oas-paired-sequence-data/parquet"
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# s3 = boto3.client("s3")
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# BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/rat_SD/SRR9179275_paired.csv.gz"
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BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/"
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for species in species_list:
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print(f"Downloading {species} files")
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# list_of_df = []
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species_url_file = os.path.join(data_dir, species + ".txt")
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with open(species_url_file, "r") as f:
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i = 0
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os.makedirs(species, exist_ok=True)
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for csv_file in f.readlines():
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print(csv_file)
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filename = os.path.basename(csv_file)
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run_id = str(re.search(r"^(.*)_[Pp]aired", filename)[1])
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url = os.path.join(BASE_URL, species, csv_file)
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# s3_key = os.path.join(S3_SRC_PREFIX, species, csv_file.strip())
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# obj = s3.get_object(Bucket=S3_BUCKET, Key=s3_key)
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run_data = pd.read_csv(
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# io.BytesIO(obj["Body"].read()),
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url,
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header=1,
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compression="gzip",
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on_bad_lines="warn",
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low_memory=False,
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)
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run_data = run_data[
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[
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"sequence_alignment_aa_heavy",
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"cdr1_aa_heavy",
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"cdr2_aa_heavy",
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"cdr3_aa_heavy",
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"sequence_alignment_aa_light",
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"cdr1_aa_light",
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"cdr2_aa_light",
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"cdr3_aa_light",
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]
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]
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run_data = run_data.dropna()
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run_data.insert(0, "data_unit", run_id)
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print(run_data.shape)
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output_path = os.path.join(species, "train_" + str(i) + ".parquet")
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run_data.to_parquet(output_path)
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# list_of_df.append(run_data)
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# species_df = pd.concat(list_of_df, ignore_index=True)
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# print(f"{species} output summary:")
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# print(species_df.head())
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# print(species_df.shape)
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# os.makedirs(species, exist_ok=True)
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# species_df.to_parquet(species, partition_cols=["data_unit"])
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zip_name = species + ".tar.gz"
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print(f"Creating {zip_name}")
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with tarfile.open(zip_name, "w:gz") as tf:
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tf.add(species, arcname="")
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# print(
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# f"Uploading {zip_name} to {os.path.join('s3://', S3_BUCKET, S3_DEST_PREFIX)}"
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# )
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# s3.upload_file(zip_name, S3_BUCKET, os.path.join(S3_DEST_PREFIX, zip_name))
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print(f"Removing {species}")
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shutil.rmtree(species)
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# print(f"Removing {zip_name}")
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# os.remove(zip_name)
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oas-paired-sequence-data.py
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: MIT-0
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"""Paired sequences from the Observed Antibody Space database"""
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import datasets
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import os
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import csv
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_CITATION = """\
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@article{Olsen_Boyles_Deane_2022,
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title={Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences},
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volume={31}, rights={© 2021 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.},
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ISSN={1469-896X}, DOI={10.1002/pro.4205},
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number={1}, journal={Protein Science}, author={Olsen, Tobias H. and Boyles, Fergus and Deane, Charlotte M.},
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year={2022}, pages={141–146}, language={en} }
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"""
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_DESCRIPTION = """\
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Paired heavy and light chain antibody sequences for multiple species.
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"""
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_HOMEPAGE = "https://opig.stats.ox.ac.uk/webapps/oas/"
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_LICENSE = "cc-by-4.0"
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_BASE_URL = "https://aws-hcls-ml.s3.amazonaws.com/oas-paired-sequence-data/raw/"
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# _URLS = {
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# "human": _BASE_URL + "human.tar.gz",
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# "rat_SD": _BASE_URL + "rat_SD.tar.gz",
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# "mouse_BALB_c": _BASE_URL + "mouse_BALB_c.tar.gz",
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# "mouse_C57BL_6": _BASE_URL + "mouse_C57BL_6.tar.gz",
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# }
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_FEATURES = datasets.Features(
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{
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"sequence_alignment_aa_heavy": datasets.Value("string"),
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"cdr1_aa_heavy": datasets.Value("string"),
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"cdr2_aa_heavy": datasets.Value("string"),
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"cdr3_aa_heavy": datasets.Value("string"),
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"sequence_alignment_aa_light": datasets.Value("string"),
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"cdr1_aa_light": datasets.Value("string"),
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"cdr2_aa_light": datasets.Value("string"),
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"cdr3_aa_light": datasets.Value("string"),
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}
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)
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class OasPairedSequenceData(datasets.GeneratorBasedBuilder):
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"""OAS paired sequence data."""
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VERSION = datasets.Version("1.2.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="human", version=VERSION, description="human"),
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datasets.BuilderConfig(name="rat_SD", version=VERSION, description="rat_SD"),
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datasets.BuilderConfig(
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name="mouse_BALB_c", version=VERSION, description="mouse_BALB_c"
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),
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datasets.BuilderConfig(
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name="mouse_C57BL_6", version=VERSION, description="mouse_C57BL_6"
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),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=_FEATURES,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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# def _split_generators(self, dl_manager):
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# urls = _URLS[self.config.name]
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# data_dir = dl_manager.download_and_extract(urls)
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# return [
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# datasets.SplitGenerator(
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# name=datasets.Split.TRAIN,
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# gen_kwargs={
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# "filepath": os.path.join(data_dir),
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# "split": "train",
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# },
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# ),
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# ]
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# def _generate_examples(self, filepath, split):
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# table = pd.read_parquet(filepath)
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# for key, row in enumerate(table.itertuples()):
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# if key == 0:
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# continue
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# yield key, {
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# "sequence_alignment_aa_heavy": row[1],
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# "cdr1_aa_heavy": row[2],
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# "cdr2_aa_heavy": row[3],
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# "cdr3_aa_heavy": row[4],
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# "sequence_alignment_aa_light": row[5],
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# "cdr1_aa_light": row[6],
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# "cdr2_aa_light": row[7],
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# "cdr3_aa_light": row[8],
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# }
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def _split_generators(self, dl_manager):
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data_unit_file = os.path.join(
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os.getcwd(), "data_units", self.config.name + ".txt"
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)
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with open(data_unit_file, "r") as f:
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urls = [
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os.path.join(_BASE_URL, self.config.name, line.strip()) for line in f
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]
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data_files = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_files,
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"split": "train",
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},
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),
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]
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def _generate_examples(self, filepaths):
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for filepath in filepaths:
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with open(filepath, "r") as f:
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reader = csv.reader(f, delimiter=",")
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for key, row in enumerate(reader):
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if key < 2:
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continue
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else:
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yield key - 2, {
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"sequence_alignment_aa_heavy": row[14],
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"cdr1_aa_heavy": row[37],
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"cdr2_aa_heavy": row[41],
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"cdr3_aa_heavy": row[47],
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"sequence_alignment_aa_light": row[113],
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"cdr1_aa_light": row[136],
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"cdr2_aa_light": row[140],
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"cdr3_aa_light": row[146],
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}
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rat_SD.tar.gz
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:1c9af9dc3c63df41e091a9d847dd8aa95fdb0d73d81024516916b78c25ee1722
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size 176753
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