gene-tidy 0.1.1

No-code cleaning of messy gene/protein identifier tables, fully offline, with explicit ambiguity handling.

gene-tidy

Clean messy gene/protein identifier tables — fully offline, fully audited, no code required.

You copy a gene list out of a paper or a supplementary Excel file and half of it is garbage: p53 instead of TP53, SEPT2 silently turned into 2-Sep, dead symbols, IDs in five different formats. Drop the file in — get back clean HGNC symbols (plus Ensembl / UniProt / Entrez / RefSeq), an audit CSV, and a paste-ready methods paragraph. Ambiguous cases are flagged, never guessed, and nothing is dropped.

pip install gene-tidy
gene-tidy messy_genes.csv --out outputs/   # -> clean / ambiguous / failed + audit + methods

What that looks like

You typed	gene-tidy gives you
p53	→ TP53 (resolved from alias)
HER2	→ ERBB2 (resolved from alias)
FRAP1	→ MTOR (resolved from previous symbol)
ENSG00000141510	→ TP53 (Ensembl gene ID)
Sep-7	→ SEPTIN7 (recovered from Excel date corruption)
2-Sep	⚠️ ambiguous: SEPTIN2 / SEPTIN6 — flagged for review, not guessed
FOOBAR1	❌ no match → failed_rows.csv (nothing silently dropped)

Inspired by HGNChelper (R), but in Python, mapping to all major IDs, with explicit ambiguity handling and Excel date-corruption recovery (SEPT2 → "2-Sep", MARCH1 → "1-Mar").

Scope & limitations

gene-tidy is HGNC-centered, offline, and reproducible: it standardises human gene/protein identifiers against a bundled static HGNC complete set and records exactly which version it used. If you need a fast, offline, auditable HGNC cleanup you can cite in a methods section, this is for you.

What it deliberately does not do — use BioMart / VEP / UniProt for these:

• Human only. No other species.
• Static, bundled HGNC subset. No live lookups, no API keys, no always-current data — the exact version is pinned and recorded on every run.
• Gene-level only. Ensembl transcript/protein IDs (ENST… / ENSP…) are detected but not resolved offline; they are flagged for manual review.
• No variant or clinical interpretation. No HGVS / ClinVar / VEP / gnomAD / liftover / genome-build detection.
• No reinterpretation of numeric Excel date serials (e.g. 44075) — they are indistinguishable from Entrez IDs and are intentionally left untouched.

Why

• Works offline, out of the box. The full HGNC complete set (all Approved gene records) ships inside the package as a gzipped TSV. No network, no API keys, no surprises — and the exact HGNC version is recorded in every run.
• Never guesses silently. One-to-many or uncertain mappings are flagged ambiguous / manual_review_required and routed to a separate file.
• Never drops a row. Clean, ambiguous, and failed rows are all accounted for.
• Reproducible. Every run emits a methods_text.txt paragraph (tool version, HGNC version + date) ready to paste into a supplementary methods section.

Install

pip install gene-tidy

Requires Python 3.10+. Dependencies: pandas, openpyxl, typer.

To install the latest development version directly from GitHub:

pip install git+https://github.com/MargoSolo/gene-tidy.git

From source (recommended for development):

git clone https://github.com/MargoSolo/gene-tidy
cd gene-tidy
pip install -e .

Quickstart (CLI)

gene-tidy input.xlsx --out outputs/

That's it. outputs/ will contain six files (see below). Works the same on .txt, .csv, .tsv, and .xlsx:

gene-tidy my_genes.txt --out outputs/
gene-tidy supp_table.csv --out outputs/

Useful flags:

gene-tidy data.xlsx -o out/ --column gene_symbol   # force the identifier column
gene-tidy data.csv  -o out/ --column symbol -c ensembl_id   # multiple columns
gene-tidy data.xlsx -o out/ --hgnc-file hgnc_complete_set.txt  # use the full HGNC set
gene-tidy --version                                 # tool + HGNC dump version

Quickstart (Python)

from gene_tidy import tidy_file, tidy_values

# Whole file -> writes the six output files, returns a result object.
result = tidy_file("supp_table.xlsx", "outputs/")
print(result.counts)   # {'total': 21, 'clean': 16, 'ambiguous': 3, 'failed': 2}

# Or clean an in-memory list of identifiers (no files written):
result = tidy_values(["TP53", "p53", "Sep-7", "ENSG00000141510", "1-Mar", "FOOBAR1"])
print(result.audit[["input_value", "approved_symbol", "match_status"]])

       input_value approved_symbol     match_status
0             TP53            TP53          matched
1              p53            TP53    matched_alias
2            Sep-7         SEPTIN7  recovered_excel
3  ENSG00000141510            TP53          matched
4            1-Mar  MARCHF1;MTARC1        ambiguous
5          FOOBAR1                          unmatched

1-Mar (ambiguous between MARCHF1 and MTARC1) lands in result.ambiguous; FOOBAR1 lands in result.failed. Nothing is dropped.

What it handles

Input	Example	Result
Approved symbol	TP53	matched → TP53
Alias symbol	p53, HER2	matched_alias (warns "resolved from alias")
Previous symbol	FRAP1, VEGF	matched_prev (warns "resolved from previous symbol")
Ensembl gene	ENSG00000141510	matched → TP53
UniProt	P38398	matched → BRCA1
Entrez	672	matched → BRCA1
RefSeq	NM_000546	matched → TP53
HGNC ID	HGNC:11998	matched → TP53
Excel date corruption	Sep-7	recovered_excel → SEPTIN7 (always warns)
Ambiguous corruption	1-Mar, 2-Sep, 1-Dec	ambiguous → e.g. MARCHF1/MTARC1, SEPTIN2/SEPTIN6 → manual review
Multiple IDs per cell	KRAS, NRAS	split and resolved independently
Case / whitespace	tp53	normalised → TP53
Duplicates	TP53 ×2	kept, flagged in warning
No match	FOOBAR1	unmatched → failed_rows.csv

Output files

Every run writes six files to --out:

File	Contents
clean_table.xlsx / clean_table.csv	confidently resolved rows
ambiguous_rows.csv	one-to-many / uncertain rows needing manual review
failed_rows.csv	unmatched and empty rows
mapping_audit.csv	every input → output, with full provenance (see below)
methods_text.txt	paste-ready methods paragraph (tool + HGNC version/date)

Columns (required schema)

input_value, detected_type, approved_symbol, hgnc_id, ensembl_gene_id, uniprot_id, entrez_id, refseq_id, match_status, warning, source_used, manual_review_required (plus source_row / source_column for traceability back to the original table).

match_status is one of: matched, matched_alias, matched_prev, recovered_excel (→ clean) · ambiguous (→ review) · unmatched, empty (→ failed).

Every table also carries per-row provenance — matched_field (which HGNC field matched: symbol / alias_symbol / prev_symbol / ensembl_gene_id / uniprot_ids / entrez_id / refseq_accession / hgnc_id / excel_recovery), match_reason (human-readable), and candidate_count (1 for a clean hit, N for ambiguous, 0 for no match). mapping_audit.csv additionally records hgnc_dump_date and gene_tidy_version on every row for full reproducibility.

Source of truth & offline guarantee

Resolution runs against a static, bundled HGNC complete set — src/gene_tidy/data/hgnc_complete_set.tsv.gz, containing all ~45,000 Approved HGNC gene records — matched against the approved symbol, alias_symbol, and prev_symbol fields. The accompanying hgnc_version.json records the source URL, HGNC license (CC0), download date, release tag, and record count; the same provenance is printed by gene-tidy --version, written into every mapping_audit.csv row, and summarised in methods_text.txt.

To use a different / newer HGNC release, pass --hgnc-file path/to/hgnc_complete_set.txt, set the GENE_TIDY_HGNC_FILE environment variable, or regenerate the bundled dump with python tools/build_hgnc_data.py hgnc_complete_set.txt. A user-supplied file is filtered to status == Approved automatically.

The package and its tests never require network access. (The test suite resolves against a tiny curated fixture in tests/fixtures/ for speed; the real bundled dump is exercised separately in tests/test_data_boundary.py.)

Real-world note: because the bundled data is the real HGNC set, genuine one-to-many cases surface honestly. For example SEPT2 is a previous symbol of SEPTIN2 and an alias of SEPTIN6, so gene-tidy reports it ambiguous rather than guessing.

Colab notebook

Zero-setup, in-browser: upload a file → run → preview clean/failed/ambiguous rows → download a ZIP of all outputs. A bundled messy_example.xlsx lets you click Run and see results immediately.

notebooks/gene_tidy_colab.ipynb

Development

pip install -e ".[dev]"   # installs pytest, build, and twine
pytest                    # 116 tests, all offline

Test coverage: ID-type detection, column detection, resolver (alias / prev / Excel-corruption / ambiguity), input/output file handling, CLI, golden-output regression on the bundled example, and a data-boundary test that loads the real bundled HGNC complete set. Most tests use a small curated fixture (tests/fixtures/hgnc_subset.tsv) so the suite runs in seconds.

To refresh the bundled HGNC data (deterministic: the same input always produces a byte-identical .tsv.gz, and the run records raw_download_sha256 + bundled_tsv_gz_sha256 in hgnc_version.json):

python tools/build_hgnc_data.py path/to/hgnc_complete_set.txt   # from a pinned file
python tools/build_hgnc_data.py --download                      # or fetch current

Attribution & citing HGNC

gene-tidy resolves identifiers using data from the HUGO Gene Nomenclature Committee (HGNC).

• Source: HGNC complete set (hgnc_complete_set.txt) from the HGNC download archive. The exact source URL, snapshot date, and SHA-256 hashes are recorded in src/gene_tidy/data/hgnc_version.json.
• Snapshot bundled in this release: see downloaded_date and bundled_tsv_gz_sha256 in src/gene_tidy/data/hgnc_version.json (also printed by gene-tidy --version and written into every mapping_audit.csv / methods_text.txt).
• License: HGNC data are released under a CC0 1.0 public-domain dedication, so they are free to redistribute; gene-tidy bundles a column-trimmed, Approved-only snapshot.
• Recommendation: in your own methods/supplementary text, cite HGNC and state the retrieval month/year of the dump you used (e.g. "HGNC complete set, retrieved June 2026, via gene-tidy v0.1.0"). The exact date and hash are in hgnc_version.json and the generated methods_text.txt.

Please cite HGNC: Seal RL, et al. Genenames.org: the HGNC resources in 2023. Nucleic Acids Res. 2023;51(D1):D1003–D1009.

License

gene-tidy itself is MIT — see LICENSE. The bundled HGNC data is CC0 (see Attribution above).