Tablassert
Extract knowledge assertions from tabular data into NCATS Translator-compliant KGX NDJSON, declaratively, with entity resolution built in and optional quality control.
Tablassert turns biomedical spreadsheets (Excel, CSV, TSV) into knowledge graphs ready for NCATS Translator. Declare how your columns map to subject-predicate-object statements in YAML; Tablassert resolves free text to standard CURIEs, attaches provenance and statistical annotations, and emits KGX-compliant nodes and edges.
Full Documentation: installation guides, tutorial, configuration reference, and API docs.
Statement of need
Biomedical knowledge lives in spreadsheets: association tables, assay results, curated gene-disease lists. Getting those rows into an NCATS Translator knowledge graph means writing an ingest that maps columns to Biolink statements, resolves free text ("TP53", "lung cancer") to standard CURIEs, and records provenance, terms of use, and statistical annotations. Today that ingest is Python code maintained per source, and entity resolution usually means calling a hosted name-resolution service at build time.
Tablassert replaces the per-source code with a declarative YAML mapping plus an embedded, offline
entity-resolution database built from RENCI BABEL exports, so a build is reproducible, auditable,
and network-free. It is aimed at Translator ingest authors and knowledge-graph data engineers who
hold tabular biomedical sources, and at bioinformatics groups that want a validated KGX output
without writing a transform pipeline. The configuration model and the Biolink/KGX contracts it
emits follow current NCATSTranslator/translator-ingests
practice, and each build can emit the Resource Ingest Guide (RIG) metadata those submissions require.
Getting help
- Questions and bug reports: open an issue at
github.com/SkyeAv/Tablassert/issues (use the
questionlabel for usage questions). Include the command you ran, its output, and yourtablassert --version. - Contributing code or docs: see CONTRIBUTING.md for setup, the local quality gates, and pull-request expectations. By participating in this project you agree to follow the Code of Conduct.
- Reference: the CLI Reference and configuration guides document every flag and field.
Quick Start
pip install "tablassert[cli]"
Given a CSV of gene-disease associations with p-values and sample sizes, declare the mapping in a
table config (table.yaml):
template:
source:
kind: text
local: ./gene-disease.csv
url: [https://example.com/data.csv]
row_slice: [1, auto]
delimiter: ","
statement:
subject: { method: column, encoding: A, prioritize: [Gene] }
predicate: associated_with
object: { method: column, encoding: B, prioritize: [Disease] }
provenance: { repo: PMID, publication: "12345678" }
annotations:
- { annotation: p_value, method: column, encoding: C }
- { annotation: study_size, method: column, encoding: D }
Wrap it in a graph config (graph.yaml) pointing at your fullmap entity-resolution database
and carrying the required rig: metadata for the generated Resource Ingest Guide. Build or
download that database once first (tablassert build-fullmap, a multi-GB download; see the
Fullmap guide), which makes ./fullmap below the
right path:
name: MY_KG
version: 1.0.0
tables:
- ./table.yaml
fullmap: ./fullmap
rig:
source_info:
infores_id: infores:my-kg
terms_of_use_info:
terms_of_use_url: https://example.org/terms
data_access_locations:
- My source downloads - https://example.org/downloads
source_status: maintained_regular_updates
ingest_info:
utility: Gene-disease associations support Translator disease-mechanism queries.
scope: Gene-disease associations extracted from tabular sources.
provenance_info:
contributions:
- "Author Name - code author, data modeling"
artifact_base_url: https://example.org/my-kg
artifact_base_path: ./published/my-kg
Build the knowledge graph:
tablassert build-kg graph.yaml
Output is one JSON object per line: nodes with Biolink categories, edges with annotations. The edge sample below is abridged; real records also carry provenance fields such as knowledge_level, agent_type, sources, and original_subject/original_object.
{"id":"HGNC:11998","name":"TP53","category":["biolink:Gene"],"in_taxon":["NCBITaxon:9606"],"provided_by":["infores:my-kg"]}
{"id":"MONDO:0008903","name":"lung cancer","category":["biolink:Disease"],"in_taxon":["NCBITaxon:9606"],"provided_by":["infores:my-kg"]}
{"id":"8f41c2d7-9b3e-35c1-a4e8-6d2f9a0c5b71","subject":"HGNC:11998","predicate":"biolink:associated_with","object":"MONDO:0008903","p_value":"1.0000e-03","statistical_significance_qualifier":"very_strongly_significant","has_supporting_studies":{"PMID:12345678":{"id":"PMID:12345678","name":"gene-disease.yaml SECTION 1","study_size":450,"has_study_results":[{"id":"row:2"}]}},"publications":["PMID:12345678"]}
See the Tutorial for the full walkthrough.
Key Features
- Declarative YAML configuration: define data transformations without writing code
- Built-in entity resolution: map free text to genes, diseases, and chemicals with standard CURIEs, taxonomic filtering, and provenance, backed by an embedded redb database
- Optional quality control: a four-stage audit (exact match, fuzzy match, abbreviation expansion, then SapBERT embeddings) flags low-confidence mappings
- KGX compliance: emits NCATS Translator-compatible node/edge NDJSON with Biolink categories and predicates
- Performance & reproducibility: lazy Polars pipelines and a deterministic UV-based development environment
Installation
pip install tablassert
To use the Python API, install Tablassert into your project's environment with pip install "tablassert" (or uv add tablassert). To get the tablassert command as a standalone tool, use
uv tool install "tablassert[cli]"; note that a tool install is isolated and does not expose the
Python API to your project. Other extras are opt-in:
| Extra | Adds | Install |
|---|---|---|
cli |
tablassert command and rich terminal progress |
pip install "tablassert[cli]" |
rt |
CPU-compatible Polars runtime | pip install "tablassert[rt]" |
aria2 |
bundled aria2c downloader, used automatically by build-fullmap when installed (Linux/Windows wheels only) |
pip install "tablassert[aria2]" |
qc |
four-stage QC audit (exact match, fuzzy match, abbreviation expansion, and SapBERT embeddings) | pip install "tablassert[qc]" |
log |
loguru-backed file/progress logging (rotation, enqueue) | pip install "tablassert[log]" |
Reaching a feature whose extra is missing never produces a bare ModuleNotFoundError: the failure
names the missing package and the install command that fixes it. QC is opt-in at build time
(build-kg --qc). See the
Installation guide for the full matrix,
per-command preflight behavior, and the
CLI Reference for every flag.
Entity Resolution API
from pathlib import Path
from tablassert.lib import resolve_many
results = resolve_many(
col="gene",
entities=["TP53", "BRCA1"],
fullmap=Path("/path/to/fullmap"),
taxon="9606",
)
# [{"original_gene": "TP53", "gene": "HGNC:11998", "gene_name": "TP53", ...}, ...]
Point resolve_many() at a fullmap database to resolve any iterable of entity strings to CURIEs,
no LazyFrame setup or NLP preprocessing required. See the
Batch Resolution API for the full reference.
Documentation
- Installation: install methods, extras, and development setup
- Tutorial: step-by-step example with synthetic data
- Use Case Gallery: real-world configuration patterns
- CLI Reference: complete command-line flag reference
- Fullmap: building and querying the entity-resolution database
- Configuration: graph and table configuration reference
- API Reference: core functions documentation
- Development: dev environment setup and contributor workflow
- Changelog: release history
Developing
uv sync --group dev --extra cli --extra qc --extra log
uv run maturin develop --manifest-path rust/Cargo.toml
make check
See CONTRIBUTING.md for the full development loop, quality gates, and pull request guidelines.
Citation
If you use Tablassert, please cite it as described in CITATION.cff. The approach is described in:
Skye Lane Goetz, Amy K. Glen, and Gwênlyn Glusman. "MicrobiomeKG: bridging microbiome research and host health through knowledge graphs." Frontiers in Systems Biology 5 (2025). doi:10.3389/fsysb.2025.1544432
License
Contributors
- Skye Lane Goetz, Institute for Systems Biology
- Gwênlyn Glusman, Institute for Systems Biology
- Jared C. Roach, Institute for Systems Biology
1 maintainer
Metadata
1 maintainer
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