orfdata

orfdata builds the protein reference for a species: the proteins annotated in a PyEnsembl genome, plus open reading frames (ORFs) that ribosome-profiling studies found translated outside those annotations. These include upstream and downstream ORFs, ORFs in other reading frames of a coding sequence, and ORFs on RNAs annotated as noncoding. Each added ORF stays bound to its PyEnsembl transcript, with its source, category and evidence.

Installation

Use Python 3.9 or later. Install orfdata and one PyEnsembl annotation:

python -m pip install 'orfdata[pyensembl]'
pyensembl install --release 115 --species human

Example

from pyensembl import EnsemblRelease
from orfdata import Proteome

genome = EnsemblRelease(115, species="human")
proteome = Proteome.from_genome(genome).with_extra_orfs(evidence="ms")
proteome.write_fasta("human-e115.fasta")
print(proteome.summary()["kinds"], len(proteome.excluded))

{'annotated': 245535, 'extra_orf': 2181} 5

This takes every protein in the Ensembl 115 annotation and adds the 2,181 bundled human ORFs that have supporting mass-spectrometry peptides and are encoded by an Ensembl 115 transcript. Each entry lists every transcript encoding it; the other 5 selected ORFs are listed in proteome.excluded with the reason. Use evidence="ribosome" for all 7,239 encoded ribosome-profiling ORFs, or a rule such as RiboSeq(min_studies=2) & MS() to set Ribo-seq and MS stringency separately (1,293 encoded ORFs). proteome.save(path) writes a checksummed snapshot that Proteome.load reads offline. From the command line:

orfdata proteome --species human --release 115 --output human-e115.fasta

A translated ORF or a detected peptide does not by itself establish a stable, functional protein. Most MS support in the bundled human catalogue comes from immunopeptidomics: peptides eluted from HLA molecules.

Species

The bundled noncanonical ORF catalogue is human GRCh38. Mouse has 48,805: Ribo-seq ORFs of Chang et al. 2025 and Yang et al. 2024, and 2,976 MS-supported ones from OpenProt 2.2; pip install "orfdata[data,build-data]" then orfdata setup --species mouse builds all three from their sources' own files. After orfdata chains download (add --accept-terms for UCSC's non-commercial mouse chains), older assemblies (GRCh37, NCBI36, GRCm38, NCBIM37) and T2T-CHM13 use these catalogues lifted to them; orfdata genomes install t2t-chm13 installs a CHM13 annotation. Dog and yeast proteomes contain their PyEnsembl-annotated proteins. load_catalog(species=...) and with_extra_orfs() pick the catalogue for a species and raise when none is available or built. Human evidence is never transferred to another species. Viral proteomes can be added with with_viral(...), but only to a proteome for one of the virus's recorded hosts. orfdata also provides RNA, ribosome and protein measurements for human, mouse, dog and yeast.

Measurement datasets

RNA, ribosome and protein measurements are separate, downloadable datasets with the same species-scoped API. Install the data extra, then download and query one:

pip install 'orfdata[data]'
orfdata datasets list
orfdata datasets download ingolia-yeast-rna-ribo

Gene RNA and canonical protein measurements do not establish noncanonical ORF abundance. See released data and gaps.

Documentation

The documentation covers:

The data roadmap tracks further species and sources. Every PR includes a version bump, lint and tests; maintainers publish with ./deploy.sh from clean main.

License

Apache 2.0. Downloadable datasets keep their sources' own terms and attribution, recorded in each release manifest.