| --- |
| 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](https://usegalaxy.eu/root?tool_id=cd_hit) 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](https://huggingface.co/docs/datasets/index) 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 |
| <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
|
|
| ## 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/) |
