Upload jolma_split.py

jolma_split.py

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+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"
+
+
+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"]
+    ]
+
+    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):
+        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")
+