Update README

README.md

@@ -32,17 +32,17 @@ configs:
     path: splits/id09/test.parquet
 ---
 
-# *SoftDis* dataset
+# SoftDis dataset
 
-*SoftDis* is a dataset for the exploration of disordered regions in 
+SoftDis is a dataset for the exploration of disordered regions in 
 protein structures, and their relations with interacting sites. 
 
-The concept of soft disorder was introduced in [[Seoane and Carbone, 2021]](https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://journals.plos.org/ploscompbiol/article%3Fid%3D10.1371/journal.pcbi.1008546&ved=2ahUKEwj43OOAmISLAxW4UKQEHRszOaIQFnoECBQQAQ&usg=AOvVaw1YXiijzojYENeuv-rxtzLD), 
+The concept of soft disorder was introduced in [(Seoane and Carbone, 2021)](https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://journals.plos.org/ploscompbiol/article%3Fid%3D10.1371/journal.pcbi.1008546&ved=2ahUKEwj43OOAmISLAxW4UKQEHRszOaIQFnoECBQQAQ&usg=AOvVaw1YXiijzojYENeuv-rxtzLD), 
 as a general term for regions in a protein identified as flexible 
 (characterized by high B-factor) or intermittently missing across different 
 X-ray crystal structures of the same sequence. The definition is derived from 
 an extensive analysis of clusters of alternative structures for very similar 
-protein sequences in the Protein Data Bankh (PDB).  
+protein sequences in the Protein Data Bank (PDB).  
 
 The current version of the dataset is based on the structures in the PDB up to 15-10-2024.
 
@@ -50,14 +50,12 @@ The current version of the dataset is based on the structures in the PDB up to 1
 ## Dataset construction
 
 To construct the SoftDis database, we retrieved protein
-structures from the PDB archive (snapshot as of 15-10-2024)
-and clustered sequences using MMSeqs2 [(Steinegger and Söding,
-2017)](https://www.nature.com/articles/nbt.3988) at 90% sequence identity and 90%
-coverage.  
+structures from the PDB archive
+and clustered sequences using MMSeqs2 [(Steinegger and Söding,2017)](https://www.nature.com/articles/nbt.3988) at 90% sequence identity and 90% coverage.  
 This process yielded 64,285 clusters encompassing a total
-of 484,044 chains belonging to 229,376 structures. The average
+of 484,044 chains, belonging to 229,376 structures. The average
 number of structures per cluster is 7.53, with a median of 3.
-Nearly half of the clusters (31,412) contains only 1 or 2 sequences,
+Nearly half of the clusters (31,412) contain only 1 or 2 sequences,
 while the largest cluster includes 1,727 homologs.  
 The representative chain for each cluster was selected from the
 experimental structure with the highest resolution or best R-value.
@@ -72,11 +70,10 @@ factor of the Cα atom, and $B$ and $σ_B$ are the mean and standard
 deviation of $B_i$ values within the chain. Additionally, residues
 were labeled as interface if they participated in protein-protein
 or protein-DNA/RNA interactions. Protein-protein binding sites
-were identified using the INTerface Builder tool [(Dequeker et al.,
-2017)](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00360), 
+were identified using the INTerface Builder tool [(Dequeker et al.,2017)](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00360), 
 where residue contacts are defined by Cα atoms within
 5 Å. Protein-DNA/RNA binding sites were determined as residues
-showing decreased accessible surface area, measured using Naccess
+showing decreased accessible surface area, measured using [Naccess](http://www.bioinf.manchester.ac.uk/naccess/)
 (Hubbard and Thornton, 1993) with a 1.4 Å probe, upon binding.
 For each site in the representative sequence of a cluster, we
 recorded the number of times it was labeled as soft disordered
@@ -85,12 +82,9 @@ as missing. Similarly, we noted the number of times each site was
 labeled as interface. 
 
 
-## Dataset Structure
-
-
 ## Usage
 
-Data for different configurations can be loaded with the following script:
+Data for different configurations can be loaded using HuggingFace `datasets` library with the following script:
 
 ```python
 from datasets import load_data
@@ -98,6 +92,84 @@ from datasets import load_data
 dataset = load_data("CQSB/SoftDis", name=config_name)
 ```
 
-Available configurations are specified in the dataset metadata.  
-If `config_name` is not specified, the default data will be loaded, containing summary information about the 64,285 clusters.
+The function returns a `datasets.DatasetDict` object, whose items can be accessed by specifying the corresponding split, as in the following
+
+```python
+# Assign splits to different datasets.Dataset objects
+train_data, val_data, test_data = load_data("CQSB/SoftDis", "id05")
+
+# Load default `train` split
+train_data = load_data("CQSB/SoftDis", split="train")
+
+# Access `train` split after loading
+data = load_data("CQSB/SoftDis", "id05")
+train_data = data['train']
+```
+
+### Available configurations
+
+Available configurations are specified in the dataset metadata. We report details about each of them in the following table:
+
+| Name | Splits | Num. samples | Description |
+|------|--------|--------------|-------------|
+| default | `train` | 64,285 | Summary data for all clusters |
+| id05 | `train`, `validation`, `test` | 26,752 \| 1,155 \| 2,523 | Clusters data filtered at 50% identity |
+| id07 | `train`, `validation`, `test` | 32,287 \| 1,146 \| 2,250 | Clusters data filtered at 70% identity |
+| id09 | `train`, `validation`, `test` | 38,253 \| 1,068 \| 2,282 | Clusters data filtered at 90% identity |
+| clusters-all | `train` | 484,044 | Detailed info for all chains in each cluster (archive) |
+| clusters | `train` | 484,044 | Detailed info for all chains in each cluster (single files) |
+
+If `config_name` is not specified, default data are loaded. 
+
+To load data for all chains, `clusters-all` configuration is recommended, since it only downloads and decompresses a single file (2.66 GB compressed). After the first download and loading, that might take some minutes, the dataset is cached locally on disk using [Arrow](https://arrow.apache.org/) framework. When needed, the cached dataset is memory-mapped directly from the disk (which offers fast lookup) instead of being loaded in memory. For more information, visit this [link](https://huggingface.co/docs/datasets/about_arrow).
+
+`cluster` configuration can instead be useful to inpect information about one or few clusters, without the need to download the full database. Necessary files can be loaded with the following syntax:
+
+```python
+dataset = load_data(
+    "CQSB/SoftDis", "clusters", data_files="clusters/train/*/4zne_E.parquet"
+)
+```
+
+Each cluster is stored as `<cluster_id>.parquet` file in a subdirectory of `clusters/train` directory. The name of the subirectory corresponds to the first 2 symbols of `<cluster_id>` (in the example, `4z`), but `*` wildcard can be also used.
+
+
+## Dataset Structure
+
+### Data instances
+
+A sample item for configurations `default`, `id05`, `id07`, `id09` has the following features:
+- 'id': Value(dtype='string'): cluster identifier
+- 'sequence': Value(dtype='string'): protein sequence for cluster representative
+- 'homologs': Sequence(feature=Value(dtype='string')): chain IDs contained in the cluster
+- 'num_homologs': Value(dtype='int64'): number of chains in the cluster
+- 'missing_frequency': Sequence(feature=Value(dtype='float32')): fraction of missing residues in the cluster for each position
+- 'soft_disorder_frequency': Sequence(feature=Value(dtype='float32')): fraction of soft-disordered residues in the cluster for each position
+- 'protein_interface_frequency': Sequence(feature=Value(dtype='float32')): fraction of residues in the cluster in a protein-protein interface for each position
+- 'nucleic_acid_interface_frequency': Sequence(feature=Value(dtype='float32')): fraction of residues in the cluster in a protein-DNA/RNA interface for each position
+- 'interface_frequency': Sequence(feature=Value(dtype='float32')): union of protein-protein and protein-DNA/RNA interfaces
+
+To convert frequencies to binary labels for classification tasks, the best option is to process data with `map()` method (see documentation [here](https://huggingface.co/docs/datasets/process#map)):
+
+```python
+# Assign positive label to soft-disordered residues
+def binarize(sample):
+    sample["soft_disorder_class"] = (
+        sample["soft_disorder_frequency"] > 0
+    ).astype(np.int64)
+    return sample
+
+dataset.set_format(type="numpy")  # convert items to numpy objects
+dataset = dataset.map(binarize)   # apply function to all entries
+```
 
+A sample for configurations `clusters` or `clusters-all` has instead the following features:
+- 'id': Value(dtype='string'): chain identifier
+- 'sequence': Value(dtype='string'): chain sequence
+- 'missing': Sequence(feature=Value(dtype='bool')): boolean list of missing residues
+- 'soft_disorder': Sequence(feature=Value(dtype='bool')): boolean list of soft-disordered residues
+- 'protein_interface': Sequence(feature=Value(dtype='bool')): boolean list of residues at protein-protein interface
+- 'nucleic_acid_interface': Sequence(feature=Value(dtype='bool')): boolean list of residues at protein-DNA/RNA interface
+- 'bfactors': Sequence(feature=Value(dtype='float64')): list of B-factor for each residue (Cα atom)
+- 'residue_ids': Sequence(feature=Value(dtype='string')): list of residue identifiers, as reported in PDB file
+- 'coords': Sequence(feature=Sequence(feature=Value(dtype='float64', id=None))): L x 3 array with Cα coordinates. Coordinates of missing residues are set to NaN.