tg2.py

import os
import json
from collections import OrderedDict
import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@article{10.1093/nar/gkaa484,
    author = {Ishida, Ryoga and Adachi, Tatsuo and Yokota, Aya and Yoshihara, Hidehito and Aoki, Kazuteru and Nakamura, \
    Yoshikazu and Hamada, Michiaki},
    title = "{RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and \
    structure information}",
    journal = {Nucleic Acids Research},
    volume = {48},
    number = {14},
    pages = {e82-e82},
    year = {2020},
    month = {06},
    abstract = "{Aptamers are short single-stranded RNA/DNA molecules that bind to specific target molecules. \
    Aptamers with high binding-affinity and target specificity are identified using an in vitro procedure called \
    high throughput systematic evolution of ligands by exponential enrichment (HT-SELEX). However, the development \
    of aptamer affinity reagents takes a considerable amount of time and is costly because HT-SELEX produces a large \
    dataset of candidate sequences, some of which have insufficient binding-affinity. Here, we present RNA aptamer \
    Ranker (RaptRanker), a novel in silico method for identifying high binding-affinity aptamers from HT-SELEX data by \
    scoring and ranking. RaptRanker analyzes HT-SELEX data by evaluating the nucleotide sequence and secondary \
    structure simultaneously, and by ranking according to scores reflecting local structure and sequence frequencies. \
    To evaluate the performance of RaptRanker, we performed two new HT-SELEX experiments, and evaluated \
    binding affinities of a part of sequences that include aptamers with low binding-affinity. In both datasets, \
    the performance of RaptRanker was superior to Frequency, Enrichment and MPBind. We also confirmed that \
    the consideration of secondary structures is effective in HT-SELEX data analysis, and that RaptRanker \
    successfully predicted the essential subsequence motifs in each identified sequence.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkaa484},
    url = {https://doi.org/10.1093/nar/gkaa484},
    eprint = {https://academic.oup.com/nar/article-pdf/48/14/e82/34130937/gkaa484.pdf},
}
"""

_DESCRIPTION = """\
PRJDB9110
https://www.ebi.ac.uk/ena/browser/view/PRJDB9110
To generate RNA aptamers against human transglutaminase 2, we have performed the high-throughput systematic evolution \
of ligands by exponential enrichment (HT-SELEX). Of the eight performed rounds, the rounds 0 to 8 have been sequenced.
"""

_URL = "https://ftp.sra.ebi.ac.uk/vol1/fastq/DRR201"

_URLS = {
    "round_0": "/".join([_URL, "DRR201861/DRR201861.fastq.gz"]),
    "round_1": "/".join([_URL, "DRR201862/DRR201862.fastq.gz"]),
    "round_2": "/".join([_URL, "DRR201863/DRR201863.fastq.gz"]),
    "round_3": "/".join([_URL, "DRR201864/DRR201864.fastq.gz"]),
    "round_4": "/".join([_URL, "DRR201865/DRR201865.fastq.gz"]),
    "round_5": "/".join([_URL, "DRR201866/DRR201866.fastq.gz"]),
    "round_6": "/".join([_URL, "DRR201867/DRR201867.fastq.gz"]),
    "round_7": "/".join([_URL, "DRR201868/DRR201868.fastq.gz"]),
    "round_8": "/".join([_URL, "DRR201869/DRR201869.fastq.gz"]),
}

_HUGGINGFACE_URLS = {
    "round_0": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201861.fastq.gz",
    "round_1": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201862.fastq.gz",
    "round_2": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201863.fastq.gz",
    "round_3": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201864.fastq.gz",
    "round_4": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201865.fastq.gz",
    "round_5": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201866.fastq.gz",
    "round_6": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201867.fastq.gz",
    "round_7": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201868.fastq.gz",
    "round_8": "https://huggingface.co/datasets/thewall/tg2/resolve/main/data/DRR201869.fastq.gz",
}

_FORWARD_PRIMER = "TAATACGACTCACTATAGGGAGCAGGAGAGAGGTCAGATG"
_REVERSE_PRIMER = "CCTATGCGTGCTAGTGTGA"
_DESIGN_LENGTH = 30

"""
  "forward_primer":"TAATACGACTCACTATAGGGAGCAGGAGAGAGGTCAGATG",
  "reverse_primer":"CCTATGCGTGCTAGTGTGA",
  "add_forward_primer":"GGGAGCAGGAGAGAGGTCAGATG",
  "add_reverse_primer":"CCTATGCGTGCTAGTGTGA",
"""

class TG2Config(datasets.BuilderConfig):
    """BuilderConfig for SQUAD."""

    def __init__(self, url, adapter_match=True, length_match=True, remove_primer=True, **kwargs):
        """BuilderConfig for SQUAD.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TG2Config, self).__init__(**kwargs)
        self.url = url
        self.adapter_match = adapter_match
        self.length_match = length_match
        self.remove_primer = remove_primer


class TG2(datasets.GeneratorBasedBuilder):
    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""

    BUILDER_CONFIGS = [
        TG2Config(name=key, url=_HUGGINGFACE_URLS[key]) for key in _URLS
    ]

    DEFAULT_CONFIG_NAME = "round_4"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "identifier": datasets.Value("string"),
                    "seq": datasets.Value("string"),
                    "count": datasets.Value("int32"),
                }
            ),
            homepage="https://www.ebi.ac.uk/ena/browser/view/PRJDB9110",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(self.config.url)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files}),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        data = OrderedDict()
        with open(filepath, encoding="utf-8") as f:
            ans = {"id": key, "count": 1}
            for i, line in enumerate(f):
                if line.startswith("@") and i%4==0:
                    ans["identifier"] = line[1:].split()[0].strip()
                elif i%4==1:
                    ans["seq"] = line.strip()
                    if self.filter_fn(ans):
                        if ans['seq'] in data:
                            data[ans['seq']]['count'] += 1
                        else:
                            data[ans['seq']] = ans
                        key += 1
                    ans = {"id": key, "count": 1}
        for item in data.values():
            yield item['id'], item

    def filter_fn(self, example):
        seq = example["seq"]
        if self.config.adapter_match:
            if not seq.startswith(_FORWARD_PRIMER) or not seq.endswith(_REVERSE_PRIMER):
                return False
        if self.config.length_match:
            if len(seq)!=_DESIGN_LENGTH+len(_FORWARD_PRIMER)+len(_REVERSE_PRIMER):
                return False
        if self.config.remove_primer:
            example["seq"] = seq[len(_FORWARD_PRIMER):len(seq)-len(_REVERSE_PRIMER)]
        return True