Datasetpublic

ConvergeBio/uniref90

UniRef90 Complete UniRef90 dataset from UniProt, converted from XML to sharded Parquet. UniRef90 clusters sequences at 90% identity, providing a non-redundant protein sequence resource that balances comprehensiveness with reduced redundancy. Part of the ConvergeBio Protein Database Collection — see also UniRef100, UniRef50, and UniClust30. Dataset Summary Clusters 188,848,220 Shards 386 Compressed size ~52 GB (zstd) Sequence lengths 11 – 49,499… See the full description on the dataset page: https://huggingface.co/datasets/ConvergeBio/uniref90.

sourceHugging Facecc-by-4.0updated 6mo agoView on Hugging Face

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Dataset Card

UniRef90

Complete UniRef90 dataset from UniProt, converted from XML to sharded Parquet. UniRef90 clusters sequences at 90% identity, providing a non-redundant protein sequence resource that balances comprehensiveness with reduced redundancy.

Part of the [ConvergeBio Protein Database Collection](https://huggingface.co/collections/ConvergeBio/protein-database) — see also UniRef100, UniRef50, and UniClust30.

Dataset Summary


Clusters	188,848,220
Shards	386
Compressed size	~52 GB (zstd)
Sequence lengths	11 – 49,499 aa (median 266, mean 351)
Members per cluster	1 – 62,973 (median 1, mean 2.8)
GO annotation coverage	MF 23.6% · BP 15.4% · CC 15.5%
Updated range	2006-10-31 to 2026-01-28

Schema

Each row represents one UniRef90 cluster with its representative sequence and metadata.

Column	Type	Description
`id`	`string`	Cluster identifier (e.g. `UniRef90_P12345`)
`name`	`string`	Cluster name from UniProt
`updated`	`string`	Last update date (`YYYY-MM-DD`)
`member_count`	`int32`	Number of sequences in the cluster
`common_taxon`	`string`	Lowest common taxon across members
`common_taxon_id`	`int32`	NCBI Taxonomy ID of common taxon
`seed_id`	`string`	ID of the seed sequence
`go_mf`	`list<string>`	GO Molecular Function terms (`GO:XXXXXXX`)
`go_bp`	`list<string>`	GO Biological Process terms
`go_cc`	`list<string>`	GO Cellular Component terms
`member_ids`	`list<string>`	All member sequence IDs
`rep_member_id`	`string`	Representative member ID
`rep_member_id_type`	`string`	ID type (e.g. `UniProtKB ID`, `UniParc ID`)
`rep_organism`	`string`	Source organism of representative
`rep_organism_tax_id`	`int32`	NCBI Taxonomy ID of representative organism
`rep_protein_name`	`string`	Protein name of representative
`rep_accessions`	`list<string>`	UniProtKB accessions of representative
`rep_uniparc_id`	`string`	UniParc ID of representative
`rep_uniref50_id`	`string`	Parent UniRef50 cluster ID
`rep_uniref100_id`	`string`	Child UniRef100 cluster ID
`rep_is_seed`	`bool`	Whether the representative is the seed sequence
`sequence`	`large_string`	Representative protein sequence (uppercase amino acid alphabet)
`sequence_length`	`int32`	Length of the sequence in residues
`sequence_crc64`	`string`	CRC64 checksum from UniProt (hex)
`sequence_xxh128`	`string`	xxHash-128 of the sequence (hex, computed at build time)

Usage

python

from datasets import load_dataset

# Stream without downloading everything
ds = load_dataset("ConvergeBio/uniref90", streaming=True)
for row in ds["train"]:
    print(row["id"], row["sequence_length"])
    break

# Or load fully
ds = load_dataset("ConvergeBio/uniref90")

Data Processing

—Source: uniref90.xml.gz from the UniProt FTP
—Parsing: Streaming XML parse with lxml.etree.iterparse, multi-process for throughput
—Integrity: xxHash-128 computed per sequence; CRC64 preserved from source XML
—Validation: Passed all tiers — schema conformance, zero null/empty sequences, xxHash roundtrip, CRC64 format, GO term format, member ID consistency, and field-by-field comparison against source XML
—Format: Sharded Parquet with zstd compression

Source & Citation

UniRef is produced by the UniProt Consortium:

Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH, UniProt Consortium. "UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches." Bioinformatics 31(6):926–932 (2015). doi:10.1093/bioinformatics/btu739

About

Built by Converge Bio — 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

UniProt data is available under CC BY 4.0.