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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 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

  • 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

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