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