Update README.md

add usage details

README.md

@@ -251,6 +251,86 @@ dataset_info:
 representative genomes across the microbial tree of life and annotate
 them functionally on a per-residue basis.
 
+
+## Quickstart Usage
+
+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
+   
+and load one of the `MPI` model, e.g.,
+
+    >>> dataset_tag = "rosetta_high_quality"
+    >>> dataset_models = datasets.load_dataset(
+      path = "RosettaCommons/MIP",
+      name = f"{dataset_tag}_models",
+      data_dir = f"{dataset_tag}_models")
+
+and inspecting the loaded 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
+        })
+    })
+
+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:
+
+    >>> from frame2seq import Frame2seqRunner
+    >>> runner = Frame2seqRunner()
+    >>> runner.design(
+      pdb_file = "target.pdb",
+      chain_id = "A",
+      temperature = 1,
+      num_samples = 5000)
+
+To run `frame2seq` on each MIP target, 
+
+    >>> for row in dataset_models:
+      print(f"Predicting sequences for id = {row$id}")
+      pdb = row$pdb
+      
+
+    >>> dataset_function_prediction = datasets.load_dataset(
+      path = "RosettaCommons/MIP",
+      name = f"{dataset_tag}_function_predictions",
+      data_dir = f"{dataset_tag}_function_predictions")
+    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).
+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()
+      columns = "term_id",
+      index = "id",
+      values = "Y_hat")
+    >>> dataset_function_prediction_wide[1:3, 1:3]
+
 ## Dataset Details
 
 ### Dataset Description
@@ -363,6 +443,10 @@ genome database across the microbial tree of life:
 
 ### 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)}}