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 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