Update README.md

README.md

@@ -5,16 +5,15 @@ pretty_name: IsItABarrel Database
 tags:
 - biology
 - chemistry
-dataset_summary: a curated database of approximately two million bacterial transmembrane
-  beta barrels (TMBBs)
-repo: rmauder/IsItABarrel
-citation_bibtex: -@article{Montezano2023, title = {General features of transmembrane
+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).
+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
@@ -27,6 +26,14 @@ configs:
     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
 dataset_info:
   config_name: TMBB_information
   features:
@@ -68,9 +75,28 @@ dataset_info:
     num_examples: 387785
   download_size: 1191909078
   dataset_size: 1447159481
+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
-a searchable curated database of approximately two million bacterial transmembrane beta barrels (TMBBs)
+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
 
@@ -90,9 +116,9 @@ then, from within python load the datasets library
 
 To load one of the IsItABarrel model datasets, use 'datasets.load_datase(...)':
 
- \>\>\> dataset\_tag \= "\<DATASET TAG\>"
+ \>\>\> dataset\_tag \= "\<TMMB_information\>"
  \>\>\> dataset \= datasets.load\_dataset(
-  path \= "\<HF PATH TO DATASET\>",
+  path \= "\<rmauder/IsItABarrel/TMBB_information\>",
   name \= f"{dataset\_tag}",
   data\_dir \= f"{dataset\_tag}")\['train'\]
 
@@ -103,28 +129,39 @@ and the dataset is loaded as a 'datasets.arrow_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('\<COLUMN NAME IN DATASET\>')
+ \>\>\> dataset.data.column('\<colum_name\>')
  \>\>\> dataset.to\_pandas()
  \>\>\> dataset.to\_parquet("dataset.parquet")
 
-### <BREIF EXAMPLE OF HOW TO USE DIFFERENT PARTS OF THE DATASET>
-
 ## Dataset Details
+Subset 1: TMBB_information
+This subset contains a csv file split into test/train/validate sets.  Each set contains 13 columns of information about each protein.
 
-### Dataset Description <DETAILED DESCRIPTION OF DATASET>
+Subset 2: fasta_files
+This subset 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.
 
-- **Acknowledgements:** <ACKNOWLEDGEMENTS>
+### 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.>
 
-- **License:** agpl-3.0
+- **Acknowledgements:** Daniel Montezano, Rebecca Bernstein, Matthew M. Copeland, and Joanna S. G. Slusky
 
-### Dataset Sources - **Repository:** <https://isitabarrel.ku.edu/search> - **Paper:** <citation>
+- **License:** agpl-3.0
 
-## Uses <DESCRIPTION OF INTENDED USE OF DATASET>
+### 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>
 
-### Out-of-Scope Use <DESCRIPTION OF OUT OF SCOPE USES OF DATASET>
 
-### Source Data <DESCRIPTION OF SOURCE DATA>
+### 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 <BIBTEX REFERENCE FOR DATASET>
+## 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
+## Dataset Card Authors
+Ryan Mauder/rmauder@butler.edu