Update dataset card: add schema, stats, citation, and Converge Bio about section

README.md

@@ -49,9 +49,9 @@ dataset_info:
 
 # UniClust30 (UniRef30)
 
-Complete [UniClust30 / UniRef30](https://uniclust.mmseqs.com/) dataset (release 2023_02) from the Söding Lab, converted from HH-suite A3M format to sharded Parquet. UniClust30 clusters UniProt sequences at 30% sequence identity and provides pre-computed multiple sequence alignments (MSAs), making it a key input for protein structure prediction (e.g. ColabFold, AlphaFold) and co-evolutionary analyses.
+Complete [UniClust30 / UniRef30](https://uniclust.mmseqs.com/) dataset (release 2023_02) from the Söding Lab, converted from HH-suite A3M format to sharded Parquet. UniClust30 clusters UniProt sequences at 30% identity and includes precomputed multiple sequence alignments (MSAs) — widely used as input for protein structure prediction (AlphaFold, ColabFold) and protein language model pretraining.
 
-**Part of the [ConvergeBio Protein Database Collection](https://huggingface.co/collections/ConvergeBio/protein-database)** — see also [UniRef100](https://huggingface.co/datasets/ConvergeBio/uniref100), [UniRef90](https://huggingface.co/datasets/ConvergeBio/uniref90), and [UniRef50](https://huggingface.co/datasets/ConvergeBio/uniref50).
+**Part of the [ConvergeBio Protein Database Collection](https://huggingface.co/collections/ConvergeBio/protein-database)** — see also [UniRef50](https://huggingface.co/datasets/ConvergeBio/uniref50), [UniRef90](https://huggingface.co/datasets/ConvergeBio/uniref90), and [UniRef100](https://huggingface.co/datasets/ConvergeBio/uniref100).
 
 ## Dataset Summary
 
@@ -59,12 +59,12 @@ Complete [UniClust30 / UniRef30](https://uniclust.mmseqs.com/) dataset (release
 |---|---|
 | **Clusters** | 36,293,491 |
 | **Shards** | 629 |
-| **Source release** | 2023_02 |
-| **Source format** | HH-suite ffindex/ffdata A3M database + cluster mapping TSV |
+| **Release** | 2023_02 |
+| **Includes** | Precomputed A3M multiple sequence alignments per cluster |
 
 ## Schema
 
-Each row represents one UniRef30 cluster with its representative sequence, full A3M alignment, and cluster membership.
+Each row represents one UniClust30 cluster with its representative sequence, MSA, and membership information.
 
 | Column | Type | Description |
 |--------|------|-------------|
@@ -86,41 +86,35 @@ from datasets import load_dataset
 # Stream without downloading everything
 ds = load_dataset("ConvergeBio/uniclust30", streaming=True)
 for row in ds["train"]:
-    print(row["cluster_id"], row["num_aligned"], row["sequence_length"])
+    print(row["cluster_id"], row["sequence_length"], row["num_aligned"])
     break
 
 # Or load fully
 ds = load_dataset("ConvergeBio/uniclust30")
 ```
 
-### Working with A3M alignments
-
-The `a3m` column contains the full multiple sequence alignment in [A3M format](https://github.com/soedinglab/hh-suite/wiki#a3m-format) (a compressed variant of FASTA). You can parse it directly or use HH-suite tools:
-
-```python
-row = next(iter(ds["train"]))
-lines = row["a3m"].split("\n")
-headers = [l for l in lines if l.startswith(">")]
-print(f"Cluster {row['cluster_id']}: {len(headers)} sequences in MSA")
-```
-
 ## Data Processing
 
-- **Source:** `UniRef30_2023_02_hhsuite.tar.gz` and `uniref_mapping.tsv.gz` from [uniclust.mmseqs.com](https://uniclust.mmseqs.com/)
-- **Parsing:** Read ffindex/ffdata A3M database; representative sequence and alignment count extracted from each A3M entry; cluster members from mapping TSV
-- **Integrity:** xxHash-128 computed per sequence; A3M-extracted sequence verified against stored sequence for every row
-- **Validation:** Passed all tiers — schema conformance, zero null/empty sequences, xxHash roundtrip, A3M format validation, member ID consistency against mapping TSV, field-by-field comparison against source ffindex/ffdata, and exact row count match (36,293,491)
+- **Source:** HH-suite ffindex/ffdata A3M database and `uniref_mapping.tsv.gz` from [uniclust.mmseqs.com](https://uniclust.mmseqs.com/)
+- **Parsing:** Direct ffindex/ffdata binary reads; membership from mapping TSV
+- **Integrity:** xxHash-128 computed per sequence; A3M representative sequence verified against extracted sequence
+- **Validation:** Passed all tiers — schema conformance, zero null/empty sequences, xxHash roundtrip, A3M format checks, A3M–sequence consistency, member ID mapping verification, and field-by-field comparison against source ffindex/ffdata
 - **Format:** Sharded Parquet with zstd compression
 
 ## Source & Citation
 
-UniClust30 is produced by the [Söding Lab](https://www.mpinat.mpg.de/soeding):
+UniClust30 is produced by the [Söding Lab](https://www.mpinat.mpg.de/soeding):
 
-> Mirdita M, von den Driesch L, Galiez C, Martin MJ, Söding J, Steinegger M.
-> "Uniclust databases of clustered and deeply annotated protein sequences and alignments."
-> *Nucleic Acids Research* 45(D1):D206–D213 (2017).
+> Mirdita M, von den Driesch L, Galiez C, Martin MJ, Söding J, Steinegger M.
+> "Uniclust databases of clustered and deeply annotated protein sequences and
+> alignments." *Nucleic Acids Res.* 45(D1):D170–D176 (2017).
 > [doi:10.1093/nar/gkw1081](https://doi.org/10.1093/nar/gkw1081)
 
+## About
+
+Built by [Converge Bio](https://converge-bio.com) — accelerating drug discovery with generative AI. Converge Bio develops foundation models for protein engineering, antibody design, and gene expression optimization, powering its computational lab products ConvergeAB, ConvergeGEO, and ConvergeCELL.
+
 ## License
 
-UniClust30 data is available under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+UniClust30 data is available under
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).