Datasets:

RosettaCommons
/

UTexasAptamer

license: cc-by-4.0
language:
  - en
tags:
  - biology
pretty_name: UT Aptamer Dataset
size_categories:
  - n<1K
dataset_info:
  features:
    - name: Year of Paper
      dtype: int64
    - name: Link to PubMed Entry
      dtype: string
    - name: Journals
      dtype: string
    - name: Journal DOI
      dtype: string
    - name: Citation
      dtype: string
    - name: Type of Nucleic Acid
      dtype: string
    - name: Name of Aptamer
      dtype: string
    - name: 'Target '
      dtype: string
    - name: Aptamer Sequence
      dtype: string
    - name: Sequence Length
      dtype: int64
    - name: 'GC Content '
      dtype: float64
    - name: Affinity
      dtype: string
    - name: Kd (nM)
      dtype: float64
    - name: Pool Type
      dtype: string
    - name: 'Pool Random Region '
      dtype: float64
    - name: Binding Buffer/Conditions
      dtype: string
    - name: 'Divalent Salt '
      dtype: string
    - name: Type of the buffer
      dtype: string
    - name: pH
      dtype: float64
    - name: Molecular weight of target
      dtype: string
    - name: Application as quoted in the referenced paper
      dtype: string
    - name: Post-selex modifications to the aptamer
      dtype: string
    - name: |+
        Additional Information

      dtype: string
    - name: Serial Number
      dtype: int64
    - name: Parent sequence serial number
      dtype: float64
    - name: Corresponding Author Name, email address
      dtype: string
    - name: >-
        Aptagen Cross Referencing(Check  Aptamer Chemistry, Affinity, Length, GC
        content, sequence)
      dtype: string
  splits:
    - name: train
      num_bytes: 1860396
      num_examples: 1262
    - name: test
      num_bytes: 222670
      num_examples: 154
    - name: validation
      num_bytes: 91736
      num_examples: 64
  download_size: 641411
  dataset_size: 2174802

UT Aptamer Dataset

This is a collection of 1480 aptamer sequences from the University of Texas Aptamer Database as of 2023. This dataset is split into three subsets (train, test, and validation) based on clustering by CD-HIT.

Clustering

Clustering was conducting using the CD-HIT: Cluster Database at High Identity with Tolerance web browser using a 40% sequence identity threshold and word size of 2. To update this dataset with new reported aptamers, splits can be determined by re-clustering.

Quickstart Usage

Install HuggingFace Datasets package

Each subset can be loaded into python using the HuggingFace datasets library. First, from the command line install the datasets library

$ pip install datasets

Optionally set the cache directory, e.g.

$ HF_HOME=${HOME}/.cache/huggingface/
$ export HF_HOME

then, from within python load the datasets library

import datasets

Load model datasets

To load one of the UTexasAptamer model datasets, use datasets.load_dataset(...):

dataset = datasets.load_dataset(f"kysie/UTexasAptamer", "train")

and the dataset is loaded as a datasets.arrow_dataset.Dataset

>>> print(dataset)
DatasetDict({
    train: Dataset({
        features: ['Year of Paper', 'Link to PubMed Entry', 'Journals', 'Journal DOI', 'Citation', 'Type of Nucleic Acid', 'Name of Aptamer', 'Target', 'Aptamer Sequence', 'Sequence Length', 'GC Content', 'Affinity', 'Kd (nM)', 'Pool Type', 'Pool Random Region', 'Binding Buffer/Conditions', 'Divalent Salt', 'Type of the buffer', 'pH', 'Molecular weight of target', 'Application as quoted in the referenced paper', 'Post-selex modification to the aptamer', 'Additional Information', 'Serial Number', 'Parent sequence serial number', 'Corresponding Author Name, email address', 'Aptagen Cross Referencing(Check Aptamer Chemistry, Affinity, Length, GC content, sequence)'],
        num_rows: 1262
    })
    test: Dataset({
        features: ['Year of Paper', 'Link to PubMed Entry', 'Journals', 'Journal DOI', 'Citation', 'Type of Nucleic Acid', 'Name of Aptamer', 'Target', 'Aptamer Sequence', 'Sequence Length', 'GC Content', 'Affinity', 'Kd (nM)', 'Pool Type', 'Pool Random Region', 'Binding Buffer/Conditions', 'Divalent Salt', 'Type of the buffer', 'pH', 'Molecular weight of target', 'Application as quoted in the referenced paper', 'Post-selex modification to the aptamer', 'Additional Information', 'Serial Number', 'Parent sequence serial number', 'Corresponding Author Name, email address', 'Aptagen Cross Referencing(Check Aptamer Chemistry, Affinity, Length, GC content, sequence)'],
        num_rows: 154
    })
    validation: Dataset({
        features: ['Year of Paper', 'Link to PubMed Entry', 'Journals', 'Journal DOI', 'Citation', 'Type of Nucleic Acid', 'Name of Aptamer', 'Target', 'Aptamer Sequence', 'Sequence Length', 'GC Content', 'Affinity', 'Kd (nM)', 'Pool Type', 'Pool Random Region', 'Binding Buffer/Conditions', 'Divalent Salt', 'Type of the buffer', 'pH', 'Molecular weight of target', 'Application as quoted in the referenced paper', 'Post-selex modification to the aptamer', 'Additional Information', 'Serial Number', 'Parent sequence serial number', 'Corresponding Author Name, email address', 'Aptagen Cross Referencing(Check Aptamer Chemistry, Affinity, Length, GC content, sequence)'],
        num_rows: 64
    })
})

which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.

dataset.data.column('<COLUMN NAME IN DATASET>')
dataset.to_pandas()
dataset.to_parquet("dataset.parquet")

Curation Rationale

This dataset has the potential for training models related to aptamer sequence and binding relationships, including prediction of binding affinities and design of aptamers.

Dataset Sources

Repository: https://zenodo.org/records/8387047
Paper: Ali Askari, Sumedha Kota, Hailey Ferrell, Shriya Swamy, Kayla S Goodman, Christine C Okoro, Isaiah C Spruell Crenshaw, Daniela K Hernandez, Taylor E Oliphant, Akshata A Badrayani, Andrew D Ellington, Gwendolyn M Stovall, UTexas Aptamer Database: the collection and long-term preservation of aptamer sequence information, Nucleic Acids Research, Volume 52, Issue D1, 5 January 2024, Pages D351–D359, https://doi.org/10.1093/nar/gkad959

Dataset Card Authors

Katie Sie katiesie/@/uw.edu

Acknowledgements

Askari, Ali; Kota, Sumedha; Ferrell, Hailey; Swamy, Shriya; Goodman, Kayla S.; Okoro, Christine C.; Spruell Crenshaw, Isaiah C.; Hernandez, Daniela K.; Oliphant, Taylor E.; Badrayani, Akshata A.; Ellington, Andrew D.; Stovall, Gwendolyn M.1

Citation

@article{Askari2023,
  title = {UTexas Aptamer Database: the collection and long-term preservation of aptamer sequence information},
  volume = {52},
  ISSN = {1362-4962},
  url = {http://dx.doi.org/10.1093/nar/gkad959},
  DOI = {10.1093/nar/gkad959},
  number = {D1},
  journal = {Nucleic Acids Research},
  publisher = {Oxford University Press (OUP)},
  author = {Askari,  Ali and Kota,  Sumedha and Ferrell,  Hailey and Swamy,  Shriya and Goodman,  Kayla S and Okoro,  Christine C and Spruell Crenshaw,  Isaiah C and Hernandez,  Daniela K and Oliphant,  Taylor E and Badrayani,  Akshata A and Ellington,  Andrew D and Stovall,  Gwendolyn M},
  year = {2023},
  month = oct,
  pages = {D351–D359}
}

License

Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/)