--- 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 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("") >>> 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