| | --- |
| | language: en |
| | license: cc-by-nc-sa-4.0 |
| | pretty_name: IsItABarrel Database |
| | tags: |
| | - biology |
| | - chemistry |
| | dataset_summary: >- |
| | a curated database of approximately two million bacterial transmembrane beta |
| | barrels (TMBBs) |
| | repo: https://isitabarrel.ku.edu/search |
| | citation_bibtex: >- |
| | article{Montezano2023, title = {General features of transmembrane beta barrels |
| | from a large database}, volume = {120}, ISSN = {1091-6490}, url = |
| | {http://dx.doi.org/10.1073/pnas.2220762120}, DOI = {10.1073/pnas.2220762120}, |
| | number = {29}, journal = {Proceedings of the National Academy of Sciences}, |
| | publisher = {Proceedings of the National Academy of Sciences}, author = |
| | {Montezano, Daniel and Bernstein, Rebecca and Copeland, Matthew M. and |
| | Slusky, Joanna S. G.}, year = {2023}, month = jul} |
| | citation_apa: >- |
| | Montezano, D., Bernstein, R., Copeland, M. M., & Slusky, J. S. G. (2023). |
| | General features of transmembrane beta barrels from a large database. |
| | Proceedings of the National Academy of Sciences of the United States of |
| | America, 120(29), e2220762120. doi:10.1073/pnas.2220762120 |
| | configs: |
| | - config_name: TMBB_information |
| | data_files: |
| | - split: train |
| | path: TMBB_information/data/train-* |
| | - split: test |
| | path: TMBB_information/data/test-* |
| | - split: validate |
| | path: TMBB_information/data/validate-* |
| | - config_name: fasta_files |
| | data_files: |
| | - split: train |
| | path: fasta_files/train.fasta |
| | - split: test |
| | path: fasta_files/test.fasta |
| | - split: validate |
| | path: fasta_files/val.fasta |
| | - config_name: unsplit_TMBB_information |
| | data_files: |
| | - split: train |
| | path: unsplit_TMBB_information/data/train-* |
| | dataset_info: |
| | - config_name: TMBB_information |
| | features: |
| | - name: Protein_ID |
| | dtype: string |
| | - name: Organism |
| | dtype: string |
| | - name: Protein_Product |
| | dtype: string |
| | - name: Sequence_Length |
| | dtype: int64 |
| | - name: SignalP5_Score |
| | dtype: float64 |
| | - name: SignalP5_Type |
| | dtype: string |
| | - name: Representative |
| | dtype: string |
| | - name: Assigned |
| | dtype: int64 |
| | - name: Protein_Type |
| | dtype: string |
| | - name: Strain |
| | dtype: string |
| | - name: Strand_Count |
| | dtype: float64 |
| | - name: AlphaFoldDB_ID |
| | dtype: string |
| | - name: Sequence |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 891734109 |
| | num_examples: 1163363 |
| | - name: test |
| | num_bytes: 277747938 |
| | num_examples: 387787 |
| | - name: validate |
| | num_bytes: 277677434 |
| | num_examples: 387785 |
| | download_size: 1191909078 |
| | dataset_size: 1447159481 |
| | - config_name: unsplit_TMBB_information |
| | features: |
| | - name: Protein_ID |
| | dtype: string |
| | - name: Organism |
| | dtype: string |
| | - name: Protein_Product |
| | dtype: string |
| | - name: Sequence_Length |
| | dtype: int64 |
| | - name: SignalP5_Score |
| | dtype: float64 |
| | - name: SignalP5_Type |
| | dtype: string |
| | - name: Representative |
| | dtype: string |
| | - name: Assigned |
| | dtype: int64 |
| | - name: Protein_Type |
| | dtype: string |
| | - name: Strain |
| | dtype: string |
| | - name: Strand_Count |
| | dtype: float64 |
| | - name: AlphaFoldDB_ID |
| | dtype: string |
| | - name: Sequence |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 1447159475 |
| | num_examples: 1938935 |
| | download_size: 1193205702 |
| | dataset_size: 1447159475 |
| | dataset2_info: |
| | config_name: fasta_files |
| | features: |
| | - name: Protein_ID |
| | dtype: string |
| | - name: sequence |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 784000000 |
| | num_examples: 2326726 |
| | - name: test |
| | num_bytes: 242000000 |
| | num_examples: 775574 |
| | - name: validate |
| | num_bytes: 242000000 |
| | num_examples: 775570 |
| | download_size: 1268000000 |
| | dataset_size: 1268000000 |
| | --- |
| | # IsItABarrel |
| | This dataset contains 1,881,712 sequences collected from 600 different bacterial proteomes with sequences ranked by their likelihood of encoding a TMBB. |
| |
|
| | ## 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 IsItABarrel model datasets, use `datasets.load_datase(...)`: |
| |
|
| |
|
| | >>> dataset = datasets.load_dataset("rmauder/IsItABarrel", "TMBB_information") |
| | |
| | and the dataset is loaded as a `datasets.arrow_dataset.Dataset` |
| |
|
| | >>> dataset |
| | <RESULT OF LOADING DATASET MODEL> |
| | |
| | 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("<colum_name>") |
| | >>> dataset.to_pandas() |
| | >>> dataset.to_parquet("dataset.parquet") |
| | |
| | ## Dataset Details |
| | Dataset 1: `TMBB_information` |
| | This dataset contains a csv file split into test/train/validate sets. Each set contains 13 columns of information about each protein. |
| |
|
| | Dataset 2: `fasta_files` |
| | This dataset contains pointers to fasta files that are already split into test/train/validate sets just like the csv files. Each fasta file contains protein ID, organism, and the fasta sequence of the protein. |
| |
|
| | Dataset 3: `unsplit_TMBB_information` |
| | This dataset contains a full unsplit csv file with information about every TMBB protein. This can be used if anyone would like to test different splits or to filter data further based on organism or TMBB type. |
| |
|
| | ### Dataset Description |
| | This dataset contains information like protein_ID and protein type for almost two million transmembrane beta barrel proteins. It contains both known proteins and predicted proteins based on prokaryotic genomes. |
| | |
| | - **Acknowledgements:** Daniel Montezano, Rebecca Bernstein, Matthew M. Copeland, and Joanna S. G. Slusky |
| | |
| | - **License:** cc-by-nc-sa 4.0 |
| | |
| | ### Dataset Sources |
| | - **Repository:** [https://isitabarrel.ku.edu/search](https://isitabarrel.ku.edu/search) |
| | - **Paper:** |
| | |
| | Montezano, D., Bernstein, R., Copeland, M. M., & Slusky, J. S. G. (2023). |
| | General features of transmembrane beta barrels from a large database. Proceedings |
| | of the National Academy of Sciences of the United States of America, 120(29), e2220762120. |
| | doi:10.1073/pnas.2220762120 |
| | |
| | |
| | ### Source Data |
| | The source data is set up as a large downloadable file that is not clustered or split into train test validate splits. |
| | |
| | ## Citation |
| | @article{Montezano2023, title = {General features of transmembrane |
| | beta barrels from a large database}, volume = {120}, ISSN = {1091-6490}, url = {http://dx.doi.org/10.1073/pnas.2220762120}, |
| | DOI = {10.1073/pnas.2220762120}, number = {29}, journal = {Proceedings of the National |
| | Academy of Sciences}, publisher = {Proceedings of the National Academy of Sciences}, |
| | author = {Montezano, Daniel and Bernstein, Rebecca and Copeland, Matthew M. and |
| | Slusky, Joanna S. G.}, year = {2023}, month = jul} |
| | |
| | ## Dataset Card Authors |
| | Ryan Mauder rmauder@butler.edu |
