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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ReadError
Message:      invalid compressed data
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/tarfile.py", line 558, in _read
                  buf = self.cmp.decompress(buf)
              zlib.error: Error -3 while decompressing data: invalid code lengths set
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2383, in __iter__
                  for key, example in self.ex_iterable:
                                      ^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 315, in __iter__
                  for key_example in islice(self.generate_examples_fn(**gen_kwargs), shard_example_idx_start, None):
                                     ~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 51, in _get_pipeline_from_tar
                  current_example[field_name] = f.read()
                                                ~~~~~~^^
                File "/usr/local/lib/python3.14/tarfile.py", line 705, in read
                  b = self.fileobj.read(length)
                File "/usr/local/lib/python3.14/tarfile.py", line 536, in read
                  buf = self._read(size)
                File "/usr/local/lib/python3.14/tarfile.py", line 560, in _read
                  raise ReadError("invalid compressed data") from e
              tarfile.ReadError: invalid compressed data

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

xdxtools-genomes

Reference genome index builds for the OTTER bisulfite, RNA-seq, and PDX workflows.

Cloning a reference release here is generally faster than rebuilding it: bismark, bowtie2, and STAR index generation takes hours on a full mammalian genome, and the result is identical between machines.

Layout

The dataset mirrors the registry layout that OTTER's reference contract declares — see reference migration — so an extracted release is a working registry entry:

genomes/<id>/<release>/
├── indexes/
│   ├── <id>_<release>_bismark.tar.gz     root: indexes/bismark/
│   ├── <id>_<release>_bowtie2.tar.gz     root: indexes/bowtie2/
│   └── <id>_<release>_star.tar.gz        root: indexes/star/
├── fasta.tar.gz                          root: fasta/
├── annotations.tar.gz                    root: annotations/
├── reference.yaml
├── manifest.json
└── checksums.sha256

Each archive's root matches the path reference.yaml records for it — indexes/bismark for an index, fasta for the sequence — so extracting into the release directory restores the structure the contract describes.

The three files outside the archives are what make a release resolvable: reference.yaml declares the assembly, aliases, and per-asset digests along with the tool that built each index; manifest.json lists every file in the release with its digest; and checksums.sha256 carries the same set in sha256sum form.

Releases

id release assembly organism dataset size
hg19 GRCh37.p13-gencode-v19 GRCh37.p13 Homo sapiens 41.2 GB
hg38 GRCh38-gencode-v44 GRCh38 Homo sapiens 40.9 GB
mm10 GRCm38-gencode-M25 GRCm38 Mus musculus 36.7 GB
mm39 GRCm39-gencode-vM39 GRCm39 Mus musculus 37.5 GB
mm9 NCBIM37-gencode-M1 NCBIM37 Mus musculus 35.9 GB

A release is always named and selected as <id>@<release>; the identifier alone does not name an immutable artifact. The sizes above are the stored archives; an extracted release is larger, since the indexes are held uncompressed on disk.

Every release is named <assembly>-gencode-<annotation-version>, and the annotations are GENCODE primary assemblies throughout.

Fetching

otter-install downloads and extracts a release into a local registry:

otter-install -reference-fetch

Configured through the environment:

variable default purpose
OTTER_REFERENCE_FETCH_RELEASES — comma-separated <id>@<release> selections
OTTER_REFERENCE_FETCH_ASSETS all restrict to a subset of bismark,bowtie2,star,fasta,annotations
OTTER_REFERENCE_FETCH_REGISTRY_ROOT ~/.otter/references where genomes/ is written
OTTER_REFERENCE_FETCH_REPO this dataset the dataset to read
OTTER_REFERENCE_FETCH_BASE_URL https://huggingface.co a mirror can be substituted
OTTER_REFERENCE_FETCH_REVISION main branch, tag, or commit

Fetching needs no credentials, because the dataset is public and ungated. That is a requirement rather than an incidental property: a gate would make the anonymous fetch fail even while the dataset still reported itself as public.

Plain curl works too, if you would rather assemble a registry by hand:

base=https://huggingface.co/datasets/fallingstar10/xdxtools-genomes/resolve/main
curl -fL -o mm39.tar.gz \
  "$base/genomes/mm39/GRCm39-gencode-vM39/indexes/mm39_GRCm39-gencode-vM39_bowtie2.tar.gz"

Verification

manifest.json lists every file in the release with its digest, and checksums.sha256 carries the same set in sha256sum form. Either can be checked directly:

cd genomes/mm39/GRCm39-gencode-vM39
python3 - <<'PY'
import hashlib, json, pathlib
root = pathlib.Path(".")
entries = json.loads((root / "manifest.json").read_text())
mismatched = 0
for entry in entries:
    digest = hashlib.sha256((root / entry["path"]).read_bytes()).hexdigest()
    if "sha256:" + digest != entry["sha256"]:
        mismatched += 1
        print("MISMATCH", entry["path"])
print(f"{len(entries)} files checked, {mismatched} mismatched")
PY

Or, once the archives are extracted, with the provider's own tooling:

sha256sum --check checksums.sha256

A release is worth verifying before use rather than after: an index that disagrees with its recorded digest is one that was corrupted in transit or mixed up with another release.

Provenance

Each release records the tool and parameters that produced every index, so a build is auditable rather than merely assumed — for example STAR 2.7.11b with sjdbOverhang: 149 for GRCm39-gencode-vM39. Indexes are not portable across STAR major versions, so read reference.yaml before mixing a fetched index with a differently-pinned pipeline.

Sequences and annotations are redistributed under their providers' terms; the GENCODE FASTA and GTF retain the licences of their respective releases.

Scope

This dataset holds reference data only. Benchmarks, fixtures, and run evidence live in fallingstar10/otter-data.

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