| 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") |
|
|
|
|
