import os.path import re import pandas as pd import datasets from functools import cached_property, cache logger = datasets.logging.get_logger(__name__) _CITATION = """\ @article{jolma2010multiplexed, title={Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities}, author={Jolma, Arttu and Kivioja, Teemu and Toivonen, Jarkko and Cheng, Lu and Wei, Gonghong and Enge, Martin and \ Taipale, Mikko and Vaquerizas, Juan M and Yan, Jian and Sillanp{\"a}{\"a}, Mikko J and others}, journal={Genome research}, volume={20}, number={6}, pages={861--873}, year={2010}, publisher={Cold Spring Harbor Lab} } """ _DESCRIPTION = """\ PRJEB3289 https://www.ebi.ac.uk/ena/browser/view/PRJEB3289 Data that has been generated by HT-SELEX experiments (see Jolma et al. 2010. PMID: 20378718 for description of method) \ that has been now used to generate transcription factor binding specificity models for most of the high confidence \ human transcription factors. Sequence data is composed of reads generated with Illumina Genome Analyzer IIX and \ HiSeq2000 instruments. Samples are composed of single read sequencing of synthetic DNA fragments with a fixed length \ randomized region or samples derived from such a initial library by selection with a sequence specific DNA binding \ protein. Originally multiple samples with different "barcode" tag sequences were run on the same Illumina sequencing \ lane but the released files have been already de-multiplexed, and the constant regions and "barcodes" of each sequence \ have been cut out of the sequencing reads to facilitate the use of data. Some of the files are composed of reads from \ multiple different sequencing lanes and due to this each of the names of the individual reads have been edited to show \ the flowcell and lane that was used to generate it. Barcodes and oligonucleotide designs are indicated in the names of \ individual entries. Depending of the selection ligand design, the sequences in each of these fastq-files are either \ 14, 20, 30 or 40 bases long and had different flanking regions in both sides of the sequence. Each run entry is named \ in either of the following ways: Example 1) "BCL6B_DBD_AC_TGCGGG20NGA_1", where name is composed of following fields \ ProteinName_CloneType_Batch_BarcodeDesign_SelectionCycle. This experiment used barcode ligand TGCGGG20NGA, where both \ of the variable flanking constant regions are indicated as they were on the original sequence-reads. This ligand has \ been selected for one round of HT-SELEX using recombinant protein that contained the DNA binding domain of \ human transcription factor BCL6B. It also tells that the experiment was performed on batch of experiments named as "AC".\ Example 2) 0_TGCGGG20NGA_0 where name is composed of (zero)_BarcodeDesign_(zero) These sequences have been generated \ from sequencing of the initial non-selected pool. Same initial pools have been used in multiple experiments that were \ on different batches, thus for example this background sequence pool is the shared background for all of the following \ samples. BCL6B_DBD_AC_TGCGGG20NGA_1, ZNF784_full_AE_TGCGGG20NGA_3, DLX6_DBD_Y_TGCGGG20NGA_4 and MSX2_DBD_W_TGCGGG20NGA_2 """ _DOWNLODE_MANAGER = datasets.DownloadManager() _RESOURCE_URL = "https://huggingface.co/datasets/thewall/DeepBindWeight/resolve/main" SELEX_INFO_FILE = _DOWNLODE_MANAGER.download(f"{_RESOURCE_URL}/ERP001824-deepbind.xlsx") PROTEIN_INFO_FILE = _DOWNLODE_MANAGER.download(f"{_RESOURCE_URL}/ERP001824-UniprotKB.xlsx") pattern = re.compile("(\d+)") URL = "https://huggingface.co/datasets/thewall/jolma_split/resolve/main" """ p70c10s61087t100 p70:70种蛋白，c10:count>10的序列，s61087：总共61087条序列，t100:小于100条序列的蛋白质划分至训练集 """ class JolmaSplitConfig(datasets.BuilderConfig): def __init__(self, protein_prefix="", protein_suffix="", max_length=1000, max_gene_num=1, aptamer_prefix="", aptamer_suffix="", **kwargs): super(JolmaSplitConfig, self).__init__(**kwargs) self.data_dir = kwargs.get("data_dir") self.protein_prefix = protein_prefix self.protein_suffix = protein_suffix self.aptamer_prefix = aptamer_prefix self.aptamer_suffix = aptamer_suffix self.max_length = max_length self.max_gene_num = max_gene_num class JolmaSubset(datasets.GeneratorBasedBuilder): SELEX_INFO = pd.read_excel(SELEX_INFO_FILE, index_col=0) PROTEIN_INFO = pd.read_excel(PROTEIN_INFO_FILE, index_col=0) BUILDER_CONFIGS = [ JolmaSplitConfig(name=key) for key in ["p70c10s61087t100", "p99c3s325414t1000", "p70c3s312303t100"] ] DEFAULT_CONFIG_NAME = "p70c10s61087t100" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("int32"), "identifier": datasets.Value("string"), "seq": datasets.Value("string"), "quality": datasets.Value("string"), "count": datasets.Value("int32"), "protein": datasets.Value("string"), "protein_id": datasets.Value("string"), } ), homepage="https://www.ebi.ac.uk/ena/browser/view/PRJEB3289", citation=_CITATION, ) @cached_property def selex_info(self): return self.SELEX_INFO.loc[self.config.name] @cached_property def protein_info(self): return self.PROTEIN_INFO.loc[self.config.name] def design_length(self): return int(pattern.search(self.protein_info["Ligand"]).group(0)) def get_selex_info(self, sra_id): return self.SELEX_INFO.loc[sra_id] def get_protein_info(self, sra_id): return self.PROTEIN_INFO.loc[sra_id] @cache def get_design_length(self, sra_id): return int(pattern.search(self.get_protein_info(sra_id)["Ligand"]).group(0)) def _split_generators(self, dl_manager): if self.config.data_dir is not None and os.path.exists(self.config.data_dir): train_file = os.path.join(self.config.data_dir, f"{self.config.name}_train.csv.gz") test_file = os.path.join(self.config.data_dir, f"{self.config.name}_test.csv.gz") else: train_file = dl_manager.download(f"{URL}/{self.config.name}_train.csv.gz") test_file = dl_manager.download(f"{URL}/{self.config.name}_test.csv.gz") return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file},), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}, ), ] def _generate_examples(self, filepath): """This function returns the examples in the raw (text) form.""" logger.info("generating examples from = %s", filepath) data = pd.read_csv(filepath) for key, row in data.iterrows(): sra_id = row["identifier"].split(":")[0] protein_info = self.get_protein_info(sra_id) proteins = protein_info["Sequence"] gene_num = protein_info["Unique Gene"] protein_id = protein_info["Entry"] protein_seq = f"{self.config.protein_prefix}{proteins}{self.config.protein_suffix}" aptamer_seq = f'{self.config.aptamer_prefix}{row["seq"]}{self.config.aptamer_suffix}' if len(protein_seq)>self.config.max_length: continue if gene_num>self.config.max_gene_num: continue if str(proteins)=="nan" or len(str(proteins))==0: continue ans = {"id": key, "protein": protein_seq, "protein_id": protein_id, "seq": aptamer_seq, "identifier": row["identifier"], "count": int(row["count"]), "quality": row['quality']} yield key, ans if __name__=="__main__": from datasets import load_dataset dataset = load_dataset("jolma_split.py", split="all")