jolma.py

import os
import json
import re
import pandas as pd
import datasets
from functools import cached_property

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
"""

_URL = "ftp://ftp.sra.ebi.ac.uk/vol1/run/"
"ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR173/ERR173154/CTCF_full_AJ_TAGCGA20NGCT_1.fastq.gz"
# _FORWARD_PRIMER = "TAATACGACTCACTATAGGGAGCAGGAGAGAGGTCAGATG"
# _REVERSE_PRIMER = "CCTATGCGTGCTAGTGTGA"
# _DESIGN_LENGTH = 30

_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")

pattern = re.compile("(\d+)")


class JolmaConfig(datasets.BuilderConfig):
    def __init__(self, length_match=True, filter_N=True, **kwargs):
        super(JolmaConfig, self).__init__(**kwargs)
        self.length_match = length_match
        self.filter_N = filter_N


class Jolma(datasets.GeneratorBasedBuilder):

    SELEX_INFO = pd.read_excel(SELEX_INFO_FILE, index_col=0)

    BUILDER_CONFIGS = [
        JolmaConfig(name=index) for index in SELEX_INFO.index
    ]

    DEFAULT_CONFIG_NAME = "ERR173157"

    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"),
                }
            ),
            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 design_length(self):
        selex_info = self.selex_info
        return int(pattern.search(selex_info["Ligand"]).group(0))

    def _split_generators(self, dl_manager):
        downloaded_files = None
        sra_id = self.config.name
        selex_info = self.selex_info
        file = selex_info['file']

        if getattr(self.config, "data_dir") is not None:
            path = os.path.join(self.config.data_dir, sra_id, file)
            downloaded_files = dl_manager.extract(path)
            logger.info(f"Load from {downloaded_files}")
        if downloaded_files is None or not os.path.exists(downloaded_files):
            downloaded_url = "/".join([_URL, sra_id[:6], sra_id, file])
            logger.info(f"Download from {downloaded_url}")
            downloaded_files = dl_manager.download_and_extract(downloaded_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
        with open(filepath, encoding="utf-8") as f:
            ans = {"id": key}
            for i, line in enumerate(f):
                if line.startswith("@") and i%4==0:
                    ans["identifier"] = line[1:].strip()
                elif i%4==1:
                    ans["seq"]  = line.strip()
                elif i%4==3:
                    ans["quality"] = line.strip()
                    if self.filter_fn(ans):
                        yield key, ans
                        key += 1
                    ans = {"id": key}

    def filter_fn(self, example):
        seq = example["seq"]
        if self.config.length_match and len(seq)!=self.design_length:
            return False
        if self.config.filter_N and "N" in seq:
            return False
        return True


if __name__=="__main__":
    from datasets import load_dataset
    dataset = load_dataset("jolma.py", name="ERR173157", split="all")
    # dataset = load_dataset("jolma.py", name="ERR173157", split="all", data_dir="/root/autodl-fs/ERP001824-461")