PESTO

Prior Evidence Status from Text and Ontologies. Given a gene and a phenotype, PESTO says how much prior evidence links them, on four ordered levels:

Two branches answer independently, and are kept apart on purpose:

• Literature. PubMed is searched under the phenotype's synonyms and the gene's aliases, Claude Haiku 4.5 sorts every hit into four relevance bands, and Claude Opus 5 reads up to twenty abstracts and spreads 100 points over the four levels, citing the PMIDs it relied on.
• Open Targets. The gene's associated traits are fetched from the Open Targets Platform, the twenty nearest to the phenotype are ranked by an embedding model (BioLORD-2023), and Claude grades how each relates to the queried phenotype. The association score of a matching trait sets the level.

This is the code of Large language model classifies prior evidence in gene–phenotype associations (Guez et al., 2026). The paper/ directory redraws every figure and table of the manuscript.

Install

pip install pesto-genetics            # or: pipx install pesto-genetics

The install is about 1.3 GB, nearly all of it PyTorch, which the Open Targets branch uses to rank traits locally. On Linux, pip's default index serves the CUDA build of torch; nothing here uses a GPU, and the CPU build is several hundred megabytes smaller:

pip install pesto-genetics --extra-index-url https://download.pytorch.org/whl/cpu

The embedding model weights (439 MB) are downloaded on first use into ~/.cache/huggingface. pesto --download-models fetches them beforehand.

Your Anthropic API key

PESTO calls Claude through the Anthropic API, with your own key, billed to your account. Create one at console.anthropic.com, then:

export ANTHROPIC_API_KEY=sk-ant-...

or put the same line, without export, in ~/.local/share/pesto/.env.

The key stays on your machine. PESTO never writes it to disk, never prints it, and never stores it in a saved run. It is read in one place, src/pesto/credentials.py, and handed only to the official anthropic client in src/pesto/services/llm_service.py, which sends it to api.anthropic.com and nowhere else. Those two files are short; read them to check.

An NCBI key (NCBI_API_KEY, optional) raises the PubMed rate limit from 3 to 10 requests a second and is handled the same way.

Use

pesto --gene DDX41 --phenotype "myelodysplastic syndrome"

prints the literature verdict, the Open Targets verdict, how many articles were found and read, and what the pair cost. --json returns everything, including the probability distribution, the justification and the supporting PMIDs.

A table of pairs (tab-separated, columns gene and phenotype) runs in parallel:

pesto --bench pairs.tsv --workers 10      # writes pesto.tsv and pesto.cost.tsv

pesto --help lists the rest.

Pipelines

A pipeline ("arm") is declared in src/pesto/flow/arms.py as a list of blocks. Its fingerprint hashes every block's parameters and prompts, and every saved run carries it, so a result says exactly which pipeline produced it.

By default PESTO asks Claude whether the phenotype is a disease, and runs:

• abstracts for a disease: PESTO as in the paper.
• abstracts-trait otherwise: the same pipeline with prompts that do not assume a disease, so replicated cohort and GWAS evidence can reach Established.

With --fulltext, it runs fulltext or fulltext-trait instead. These add a Europe PMC search for articles that report the gene only in their body (a results table, a supplementary file) and append the passages naming it for the reader. PubMed cannot return those articles, and the deciding paper is often one of them. On 20 GWAS associations whose deciding article names the gene only in its body, abstracts placed 1 at Existing or above and fulltext 18, and both kept all 20 negative controls at Novel or Hypothesized (Supplementary Figure 3). fulltext was not used for the analyses of the paper, and fulltext-trait has not been benchmarked.

The other arms reproduce the paper's comparisons: titles (titles only), knowledge (the model's own knowledge, no search), and abstracts-bare, abstracts-open and abstracts-score (reading-prompt ablations).

Cost

At Anthropic list prices, the literature branch costs about $0.07–0.11 a pair with abstracts (Extended Data Fig. 2 and Supplementary Figure 3), and 2–3% more with fulltext; the Open Targets branch about $0.03. Well-studied genes cost more. A pair already answered by the same pipeline is read from the cache and costs nothing. With --bench, the cost of every pair is written to pesto.cost.tsv as it runs.

Where things are written

Runs are saved under ~/.local/share/pesto (or $PESTO_PROJECT_ROOT), one JSON per pair with the corpus, the prompts and the model's answer.

Reproducing the paper

See paper/README.md. Every figure and table is redrawn from the tables in paper/results/ without calling any model. The saved model answers behind them are on Zenodo: doi:10.5281/zenodo.23230222.

Tests

Free, no model calls, no network:

python3 tests/replay.py           # fingerprints, prompts, selection rules, parsing
python3 tests/script_imports.py   # every paper script still imports

Citation

Guez J, Auwerx C, Lu W, Satterstrom FK, Berkowitz J, Fu JM, Betancur C, Talkowski ME, Daly MJ, Karczewski KJ. Large language model classifies prior evidence in gene–phenotype associations. 2026. Code and data: doi:10.5281/zenodo.23230222.

Licence

Code: MIT. Tables and model answers produced for the paper: CC-BY 4.0. Third-party content they include keeps the terms of its source.