Update README.md

README.md

@@ -246,6 +246,8 @@ them functionally on a per-residue basis.
 
 ## 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
 
@@ -259,70 +261,54 @@ Optionally set the cache directory, e.g.
 then, from within python load the datasets library
 
     >>> import datasets
+
+### Load model datasets
    
-and load one of the `MPI` model, e.g.,
+To load one of the `MPI` model datasets, use `datasets.load_dataset(...)`:
 
     >>> dataset_tag = "rosetta_high_quality"
     >>> dataset_models = datasets.load_dataset(
       path = "RosettaCommons/MIP",
       name = f"{dataset_tag}_models",
-      data_dir = f"{dataset_tag}_models")
+      data_dir = f"{dataset_tag}_models")['train']
     Resolving data files: 100%|█████████████████████████████████████████| 54/54 [00:00<00:00, 441.70it/s]
     Downloading data: 100%|███████████████████████████████████████████| 54/54 [01:34<00:00,  1.74s/files]
     Generating train split: 100%|███████████████████████| 211069/211069 [01:41<00:00, 2085.54 examples/s]
     Loading dataset shards: 100%|███████████████████████████████████████| 48/48 [00:00<00:00, 211.74it/s]
 
-and inspecting the loaded dataset
+and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
 
     >>> dataset_models
-    DatasetDict({
-        train: Dataset({
-            features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'],
-            num_rows: 211069
-        })
+    Dataset({
+        features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'],
+        num_rows: 211069
     })
 
-many structure-based pipelines expect a `.pdb` file as input. For example, `frame2seq` takes in a structure
-and generates a sequence for the backbone. The `frame2seq` can be installed using `pip` from the command line:
-
-    $ pip install frame2seq
-
-Then used from within python:
+which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
 
-    >>> from frame2seq import Frame2seqRunner
-    >>> runner = Frame2seqRunner()
-    >>> runner.design(
-      pdb_file = "target.pdb",
-      chain_id = "A",
-      temperature = 1,
-      num_samples = 5000)
+    >>> dataset_models.data.column('pdb')
+    >>> dataset_models.to_pandas()
+    >>> dataset_models.to_parquet("dataset.parquet")
 
-To run `frame2seq` on each MIP target, 
-
-    >>> for pdb in dataset_models.data['train'].column('pdb'):
-      pdb.str
-      print(f"Predicting sequences for id = {row$id}")
-      pdb = row$pdb
-      
+### Load Function Predictions
+Function predictions are generated using `DeepFRI` across 
 
     >>> dataset_function_prediction = datasets.load_dataset(
       path = "RosettaCommons/MIP",
       name = f"{dataset_tag}_function_predictions",
-      data_dir = f"{dataset_tag}_function_predictions")
+      data_dir = f"{dataset_tag}_function_predictions")['train']
     Downloading readme: 100%|████████████████████████████████████████| 15.4k/15.4k [00:00<00:00, 264kB/s]
     Resolving data files: 100%|██████████████████████████████████████| 219/219 [00:00<00:00, 1375.51it/s]
     Downloading data: 100%|█████████████████████████████████████████| 219/219 [13:04<00:00,  3.58s/files]
     Generating train split: 100%|████████████| 1332900735/1332900735 [13:11<00:00, 1684288.89 examples/s]
     Loading dataset shards: 100%|██████████████████████████████████████| 219/219 [01:22<00:00,  2.66it/s]
 
-this loads the `>1.3B` function predictions (xxx targets x yyyy terms from the GO and EC ontologies).
+this loads the `>1.3B` function predictions for all 211069 targets for the GO and EC ontology terms.
 The predictions are stored in long format, but can be easily converted to a wide format using pandas:
 
-    >>> dataset_function_prediction
-
     >>> import pandas
     >>> dataset_function_prediction_wide = pandas.pivot(
-      dataset_function_prediction.data['train'].select(['id', 'term_id', 'Y_hat']).to_pandas()
+      dataset_function_prediction.data.select(['id', 'term_id', 'Y_hat']).to_pandas(),
       columns = "term_id",
       index = "id",
       values = "Y_hat")
@@ -380,28 +366,13 @@ protein structure databases such as the EBI AlphaFold database because it consis
 proteins from Archaea and Bacteria, whose protein sequences are generally shorter
 than Eukaryotic.
 
-### Direct Use
-This dataset could be used to train representation models of protein structure
-
--
-
-
 ### Out-of-Scope Use
 While this dataset has been curated for quality, in some cases the predicted structures
 may not represent physically realistic conformations. Thus caution much be used when using
 it as training data for protein structure prediction and design.
 
-## Dataset Structure
-    microbiome_immunity_project_dataset
-      dataset
-        dmpfold_high_quality_function_predictions
-          DeepFRI_MIP_<chunk-index>_<gene-ontology-prefix>_pred_scores.json.gz
-        dmpfold_high_quality_models
-          MIP_<MIP-ID>.pdb.gz.pdb.gz
-
 
 ### Source Data
-
 Sequences were obtained from the Genomic Encyclopedia of Bacteria and Archaea
 ([GEBA1003](https://genome.jgi.doe.gov/portal/geba1003/geba1003.info.html)) reference
 genome database across the microbial tree of life:
@@ -419,35 +390,6 @@ genome database across the microbial tree of life:
 > sequence space is still far from saturated, and future endeavors in this direction will continue to be a
 > valuable resource for scientific discovery.
 
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-{{ data_collection_and_processing_section | default("[More Information Needed]", true)}}
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-{{ source_data_producers_section | default("[More Information Needed]", true)}}
-
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-{{ bias_risks_limitations | default("[More Information Needed]", true)}}
-
-### Recommendations
-
-
-    
-
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
-
 ## Citation
 
     @article{KoehlerLeman2023,
@@ -464,7 +406,5 @@ genome database across the microbial tree of life:
       month = apr
     }
 
-
-
 ## Dataset Card Authors
 Matthew O'Meara (maom@umich.edu)