hpo-downloader 1.1.0

Preprocessing

For the pre-processing you have to retrieve the uniprot mapping files by asking directly to the Uniprot team since each mapping is aroung 17GB. Let’s save each file in a directory within this repository called "mapping/{month}/idmapping.dat.gz".

Cache for the pre-processing results is available within the python package, so there is no need to retrieve the original files unless you need to fully reproduce the pipeline.

For each release, we have to retrieve the "GeneID" and the human uniprot_IDs, and we can do so using zgrep.

zgrep "GeneID" mapping/{month}/idmapping.dat.gz > gene_id.tsv
zgrep "HUMAN" mapping/{month}/idmapping.dat.gz > human_id.tsv

Now we have to map in a non-bijective way uniprot IDs to GeneIDs on the uniprot ACs. We can use the package method non_unique_mapping.

from hpo_downloader.utils import non_unique_mapping
import pandas as pd

gene_id = pd.read_csv(
    f"mapping/{month}/gene_id.tsv",
    sep="\t",
    header=None,
    usecols=[0, 2]
)
gene_id.columns = ["uniprot_ac", "gene_id"]
human_id = pd.read_csv(
    f"mapping/{month}/human_ids.tsv",
    sep="\t",
    header=None,
    usecols=[0, 2]
)
human_id.columns = ["uniprot_ac", "uniprot_id"]
non_unique_mapping(gene_id, human_id, "uniprot_ac").to_csv(
    f"hpo_downloader/uniprot/data/{month}.tsv.gz",
    sep="\t",
    index=False
)

Package usage examples

To generate the complete mapping (optionally filtering only for Uniprot IDs within CAFA4) proceed as follows:

from hpo_downloader import mapping

my_mapping = mapping(
    month="november"
)

my_mapping_cafa_only = mapping(
    month="november",
    cafa_only=True
)

The obtained pandas DataFrames look as follows:

HPO mappings: October, November, December

HPO mappings (CAFA4 only): October (CAFA only), November (CAFA only), December (CAFA only)