Upload tg2.py

tg2.py

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+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 = "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/DRR201"
+
+_URLS = {
+    "round_0": os.path.join(_URL, "DRR201861/DRR201861.fastq.gz"),
+    "round_1": os.path.join(_URL, "DRR201862/DRR201862.fastq.gz"),
+    "round_2": os.path.join(_URL, "DRR201863/DRR201863.fastq.gz"),
+    "round_3": os.path.join(_URL, "DRR201864/DRR201864.fastq.gz"),
+    "round_4": os.path.join(_URL, "DRR201865/DRR201865.fastq.gz"),
+    "round_5": os.path.join(_URL, "DRR201866/DRR201866.fastq.gz"),
+    "round_6": os.path.join(_URL, "DRR201867/DRR201867.fastq.gz"),
+    "round_7": os.path.join(_URL, "DRR201868/DRR201868.fastq.gz"),
+    "round_8": os.path.join(_URL, "DRR201869/DRR201869.fastq.gz"),
+}
+
+_FORWARD_PRIMER = "TAATACGACTCACTATAGGGAGCAGGAGAGAGGTCAGATG"
+_REVERSE_PRIMER = "CCTATGCGTGCTAGTGTGA"
+_DESIGN_LENGTH = 30
+
+
+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=_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
+
+
+if __name__=="__main__":
+    from datasets import load_dataset
+    dataset = load_dataset("tg2.py", split="all")
+    from itertools import s
+