pysec2pri 0.1.4

Secondary to primary identifier mapping

pySec2Pri

Create and use mapping files for secondary (retired/withdrawn) biological database identifiers and labels to primary (current) identifiers and labels.

Outputs mappings in SSSOM format by default. Subjects are secondary, objects are primary.

Installation

uv pip install pysec2pri

Or install from source:

uv pip install git+https://github.com/jmillanacosta/pysec2pri.git

Quick Start

Generating mapping sets

To obtain the secondary to primary identifier SSSOM mapping set for ChEBI:

pysec2pri chebi

This will automatically download the latest ChEBI release and generate an SSSOM mapping file in your current directory.

To process locally and specify the output:

pysec2pri chebi ChEBI_complete_3star.sdf --output my_mappings.sssom.tsv

For more options and help on any command:

pysec2pri --help
pysec2pri chebi --help

The default output is in SSSOM (Simple Standard for Sharing Ontology Mappings) TSV format.

Updating IDs and labels

A generated mapping set can be used to update IDs and labels in Python:

from pysec2pri import generate_chebi_synonyms, resolve_labels
cs = generate_chebi_synonyms()
resolve_labels(["Glucose", "ATP", "Guanine"], cs)

Or from the command line, given a TSV file gene_ex.tsv:

gene	data
HGNC:131	3.5

Resolve the gene column to primary HGNC IDs (a new _primary column is added):

pysec2pri update-ids gene_ex.tsv hgnc --at gene -o gene_ex_primary.tsv
# gene        data    gene_primary
# HGNC:131    3.5     HGNC:145

The same pattern works for labels with update-labels, and multiple columns can be resolved by repeating --at:

pysec2pri update-ids data.tsv hgnc --at gene_id --at related_gene_id

To skip regenerating the mapping set, pass a pre-built mapping file:

pysec2pri hgnc ids  # outputs hgnc_{version}_sssom.tsv
pysec2pri update-ids gene_ex.tsv hgnc --at gene --mapping hgnc_{version}_sssom.tsv

Ambiguous mappings (where a deprecated ID or label serves as a recommended for another entity) are not resolved, but flagged for users to solve them manually. If the input file has a column of known aliases or synonyms for each row, pass it as a hint to resolve ambiguous names automatically:

pysec2pri update-ids data.tsv hgnc --at gene_id --synonyms gene_aliases
# Pairs gene_aliases hints with gene_id; repeat --at X--synonyms Y for more columns.

A subset with ambiguous mappings only can be generated like:

pysec2pri ambiguous hgnc-labels

Mapping types

Deprecations (IDs)

A deprecated ID is mapped to its replacement via IAO:0100001 ("term replaced by"). Each row is 1-to-1: one secondary subject_id : one primary object_id.

flowchart LR
    D["subject_id (deprecated)"]
    P["object_id (primary)"]
    D -->|"term replaced by"| P

Ambiguity happens when the same ID appears as both a subject_id (secondary) and an object_id (primary) across different mappings.

flowchart LR
    A["A (primary of C and secondary of B)"] -->|term replaced by| B["B (primary)"]
    C["C (secondary)"] -->|term replaced by| A

Labels

The same 1-to-1 pattern applies to label (or symbol) mappings: a previous or alias label (subject_label) maps to the current label (object_label) of the same entity via IAO:0100001.

Ambiguity appears when the same label is both a subject_label (previous name, secondary) and an object_label (current name, primary) across different mappings.

Aliases / synonyms

Alias mappings use oboInOwl:hasExactSynonym. The alias is the subject_label and the authoritative name is the object_label/object_id.

flowchart LR
    A["subject_label (alias / synonym)"]
    P["object_label/object_id (primary)"]
    A -->|"oboInOwl:hasExactSynonym"| P

Resolving ambiguity with alias/synonym hints

When a name is ambiguous, alias mappings are used as evidence. For each candidate interpretation the resolver checks whether any user-supplied hint matches a known alias of that candidate's primary entity. A hit on the secondary candidate's target confirms the name is being used as a previous name; a hit on the primary candidate's own aliases confirms it is already current.

flowchart TD
    Name["ambiguous name"]
    Hint["Alias hint"]
    Check{"Hint matches alias of…"}
    SecPath["Replacement target: replace"]
    PriPath["Name itself: keep"]
    Blank["Neither: flag for manual review"]
    Name --> Check
    Hint -.-> Check
    Check -->|secondary candidate| SecPath
    Check -->|primary candidate| PriPath
    Check -->|no match| Blank

Disambiguation with context (label / id / xref)

Alias hints are one kind of context: a per-row piece of independent evidence that helps decide which entity an ambiguous name actually means. update_ids and update_labels support three kinds, via pysec2pri.context.ContextSpec:

• label -- an alias/synonym string (the synonyms=/--synonyms shown above).
• id -- a related/foreign identifier string, matched the same way.
• xref -- a cross-reference token (e.g. an Ensembl ID) resolved through an independent crosswalk table (pysec2pri.context.XrefMapping), rather than the mapping set's own alias index.

All three only ever touch cells already flagged ambiguous, and every attempt can be written to an auditable decision log:

from pysec2pri import generate_hgnc_labels, update_labels
from pysec2pri.context import load_xref_mapping

label_ms = generate_hgnc_labels()
ensembl_to_hgnc = load_xref_mapping("ensembl_to_hgnc.tsv")  # subject_id/object_id/object_label

resolved = update_labels(
    df, label_ms, at="gene_name",
    xref="ensembl",                # column with each row's Ensembl ID
    xref_mapping=ensembl_to_hgnc,
    report_path="decisions.tsv",   # stage, token, predicate_id, candidate, accepted, reason
)

xref_predicates restricts which SSSOM-style equivalence predicates from the crosswalk are trusted (by default any predicate is accepted, and an unannotated record is assumed to be an equivalence). context= accepts a list of ContextSpec for cases the synonyms=/xref= shortcuts don't cover (e.g. several cross-reference columns); specs are tried in order and the first one that resolves a cell wins.

The same options are available on the CLI:

pysec2pri update-labels genes.tsv hgnc --at gene_name \
  --xref ensembl --xref-source hgnc_custom --xref-on ensembl \
  --report decisions.tsv

--xref-source/--xref-on auto-download a crosswalk that a datasource config suggests (see xref_sources in config/hgnc.yaml) instead of requiring a pre-built --xref-file. Run pysec2pri validate-config to check that every shipped config (including its xref_sources) is well-formed.

See notebooks/hgnc_ensembl_context_showcase.ipynb for a full, real-data walkthrough (GTEx gene symbols disambiguated against HGNC's own Ensembl crosswalk).

crosswalk: a direct identifier lookup helper

For the common case of "map this one column of identifiers to an HGNC ID (or back to a symbol)", crosswalk is a thin wrapper over the same machinery:

pysec2pri crosswalk --value TP53 --from symbol --to hgnc_id
pysec2pri crosswalk genes.tsv --from ensembl --to hgnc_id --at ensembl_id -o out.tsv

from pysec2pri import crosswalk
crosswalk("TP53", frm="symbol", to="hgnc_id")          # {'TP53': 'HGNC:11998'}
crosswalk("ENSG00000141510", frm="ensembl", to="symbol")  # via HGNC's own crosswalk

Documentation

Full documentation: https://pysec2pri.readthedocs.io/

Supported Databases

Datasource	license	citation
ChEBI	CC BY 4.0.	Hastings J, Owen G, Dekker A, et al. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research. 2016 Jan;44(D1):D1214-9. DOI: 10.1093/nar/gkv1031. PMID: 26467479; PMCID: PMC4702775.
HMDB	CC BY 4.0	Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Dizon R, Sayeeda Z, Tian S, Lee BL, Berjanskii M, Mah R, Yamamoto M, Jovel J, Torres-Calzada C, Hiebert-Giesbrecht M, Lui VW, Varshavi D, Varshavi D, Allen D, Arndt D, Khetarpal N, Sivakumaran A, Harford K, Sanford S, Yee K, Cao X, Budinski Z, Liigand J, Zhang L, Zheng J, Mandal R, Karu N, Dambrova M, Schiöth HB, Greiner R, Gautam V. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022 Jan 7;50(D1):D622-D631. doi: 10.1093/nar/gkab1062. PMID: 34986597; PMCID: PMC8728138.
HGNC	link	Seal RL, Braschi B, Gray K, Jones TEM, Tweedie S, Haim-Vilmovsky L, Bruford EA. Genenames.org: the HGNC resources in 2023. Nucleic Acids Res. 2023 Jan 6;51(D1):D1003-D1009. doi: 10.1093/nar/gkac888. PMID: 36243972; PMCID: PMC9825485.
NCBI	link	Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, Connor R, Funk K, Kelly C, Kim S, Madej T, Marchler-Bauer A, Lanczycki C, Lathrop S, Lu Z, Thibaud-Nissen F, Murphy T, Phan L, Skripchenko Y, Tse T, Wang J, Williams R, Trawick BW, Pruitt KD, Sherry ST. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2022 Jan 7;50(D1):D20-D26. doi: 10.1093/nar/gkab1112. PMID: 34850941; PMCID: PMC8728269.
UniProt	CC BY 4.0	UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021 Jan 8;49(D1):D480-D489. doi: 10.1093/nar/gkaa1100. PMID: 33237286; PMCID: PMC7778908.
Wikidata		Vrandecic, D., Krotzsch, M. Wikidata: a free collaborative knowledgebase. Communications of the ACM. 2014. doi: 10.1145/2629489.

License

MIT License. See LICENSE for details.