license: cc-by-4.0
pretty_name: UniRef90
size_categories:
- 100M<n<1B
task_categories:
- other
language:
- en
tags:
- biology
- proteins
- sequences
- fasta
- uniref
- clustering
UniRef90
Normalized FASTA shards of UniRef90 cluster representative sequences (90% sequence identity threshold).
Processed and uploaded by the MegaData post-download pipeline (internal repo). Original source: https://www.uniprot.org/help/uniref.
Statistics
| Source files | 1 |
| Shards | 189 |
| Compressed shard bytes | 38.65 GiB (41,498,595,315) |
| Records (per-source manifest sum) | 188,848,220 |
| Residues (per-source manifest sum) | 66,359,825,357 |
Aggregate manifest total_records |
188848220 |
Layout
.
├── _MANIFEST.json # aggregate manifest written by the pipeline
├── manifests/<source_slug>.json # per-source manifest (records, residues, shards)
├── metadata/<source_slug>.records.jsonl # per-record provenance
└── sequences/<source_slug>/shard-NNNNNN.fasta.zst
<source_slug> corresponds 1:1 with an upstream source archive; e.g.
sequence_uniprotkb_uniprot_sprot.fasta.gz.
Loading
Stream every shard of one source (replace <source_slug> with the directory of
interest under sequences/):
hf download LiteFold/UniRef90 --repo-type dataset \
--include 'sequences/<source_slug>/shard-*.fasta.zst' \
--local-dir ./uniref90
zstd -dc ./uniref90/sequences/<source_slug>/shard-*.fasta.zst | head
Programmatic streaming with zstandard:
from huggingface_hub import snapshot_download
from pathlib import Path
import zstandard as zstd
local = snapshot_download(
repo_id="LiteFold/UniRef90",
repo_type="dataset",
allow_patterns=["sequences/*/shard-*.fasta.zst"],
)
dctx = zstd.ZstdDecompressor()
for shard in sorted(Path(local).rglob("shard-*.fasta.zst")):
with shard.open("rb") as f, dctx.stream_reader(f) as reader:
buf = b""
while chunk := reader.read(1 << 20):
buf += chunk
*lines, buf = buf.split(b"\n")
for line in lines:
... # naive splitter; swap in your FASTA parser
License
CC BY 4.0 (UniProt Consortium).
Citation
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):926-32, 2015.
Provenance
Built from the local manifest entry uniref90 of manifests/atlas_download_plan.json.
Pipeline source: megadata-post normalize --dataset uniref90.