Score variants with public Encodon

Run general masked-codon mutation_prediction only. This produces a research signal, not a clinical diagnosis or an expression-direction prediction.

Instructions

First confirm CodonFM or Encodon context in the user's request or established conversation. If that context is missing, ask for any missing variant details and the intended analysis before choosing a model or inspecting model-specific files. The presence of this skill or source files alone does not establish the user's intent.

For source reviews and command preparation, inspect the supplied source and metadata without installing the ML runtime. Use an available Python 3 interpreter with standard-library zipfile, json, and csv; do not assume the python alias or unzip exists. Read archive members directly with ZipFile.namelist() and ZipFile.read() where possible. If extraction is needed, use a fresh directory from tempfile.mkdtemp() or mktemp -d and preserve existing checkouts and scratch directories. Check whether rg is available; use grep or Python if it is absent. Read the source sections needed for the requested command or compatibility question.

Check whether the request is executable in public v1 before installing or downloading anything. For synonymous-codon aggregation or Decodon, inspect the parser and model configuration, explain the missing feature, and finish. Do not implement the missing workflow, search private code, or keep retrying unsupported commands.

Resolve the variant CSV, checkpoint, and output directory from the request and available files. Validate inputs before inference. Execution requires the project's ML dependencies and a compatible NVIDIA GPU. If a required resource is unavailable, return the validated inputs where possible and a command with the missing prerequisite identified. When scoring is requested and resources are ready, execute and verify the score arrays. A request for preparation ends with the inputs and command. If variants are missing, report the required schema; do not invent variants or silently switch to a public dataset.

Default to the public 80M checkpoint for demonstrations: nvidia/NV-CodonFM-Encodon-80M-v1, revision 399ca9fe17b57941a7bebc6788033919b417413c, file NV-CodonFM-Encodon-80M-v1.safetensors and sibling config.json. Reuse an existing checkpoint or download it when needed for the requested work. Preserve an explicitly requested model size.

Preflight

1. Confirm src/runner.py, src/data/mutation_dataset.py, and src/inference/encodon.py exist.
2. Accept only encodon_80m, encodon_600m, or encodon_1b as --model_name. The public parser lists larger names, but its model configuration does not implement them.
3. For model execution, require a .ckpt file, or a .safetensors file with sibling config.json. Input preparation can use a planned path.
4. Validate the CSV headers before starting a GPU job.

Inputs

Require these CSV columns:

• id: unique row identifier.
• ref_seq: reference coding sequence, not genomic DNA with introns, UTR-only sequence, or protein sequence.
• ref_codon and alt_codon: three-nucleotide codons.
• codon_position: zero-based codon position relative to the CDS.

With --extract-seq, MutationDataset extracts an appropriate sequence window from ref_seq; it does not derive or require alt_seq.

Before running, normalize sequences and codons to uppercase DNA (A/C/G/T), require CDS lengths divisible by three, and check every row satisfies:

0 <= codon_position < len(ref_seq) / 3
ref_seq[3 * codon_position : 3 * codon_position + 3] == ref_codon

The public extractor asserts the second condition and otherwise stops the job.

Examples

Set CODONFM_DATA_PATH to the variant CSV, CODONFM_CHECKPOINT_PATH to the checkpoint, and CODONFM_RUN_DIR to your chosen output directory:

Use the interpreter from the configured ML environment for inference. The example uses python; substitute that environment's interpreter path if the alias is unavailable.

python -m src.runner eval \
    --exp_name variant_scoring \
    --model_name encodon_80m \
    --checkpoint_path "$CODONFM_CHECKPOINT_PATH" \
    --data_path "$CODONFM_DATA_PATH" \
    --process_item mutation_pred_mlm \
    --dataset_name MutationDataset \
    --task_type mutation_prediction \
    --extract-seq \
    --mask_mutation \
    --num_nodes 1 \
    --num_gpus 1 \
    --num_workers 0 \
    --val_batch_size 2 \
    --out_dir "$CODONFM_RUN_DIR" \
    --predictions_output_dir "$CODONFM_RUN_DIR/predictions"

Do not remove --mask_mutation: without it, the reference codon remains visible at the scored position and invalidates masked-codon LLR scoring. For preparation requests, inspect the CSV directly against the input schema and reference-position checks above, then report the rows checked and provide the scoring command. Extra columns are allowed; use --ref_seq_col if the reference sequence has a different column name. These checks do not require the ML runtime. The command above performs inference when resources are ready.

The existing --dryrun optionally builds runtime configuration and skips execution. It requires the ML dependencies, can create the prediction directory, and does not read the CSV or load weights. Do not use it as evidence that inputs, checkpoint compatibility, or prediction quality have been validated.

Outputs

--predictions_output_dir receives:

• ref_likelihoods_merged.npy
• alt_likelihoods_merged.npy
• likelihood_ratios_merged.npy
• ids_merged.npy

Load the arrays with NumPy and align scores by ids_merged.npy. The reported LLR is log p(ref_codon) - log p(alt_codon); a larger positive value means the alternate codon is less probable in context. It does not say whether expression goes up or down.

Reporting

Keep the final answer concise and self-contained, with the requested command or compatibility conclusion near the start. For command preparation, include each row's validation result, the complete command, all four output filenames, and the LLR definition and sign interpretation. Cite the inspected source locations for the command, outputs, and scoring semantics. State whether inference ran; report numerical scores only when execution produced them.

Boundaries

• General mutation_prediction handles both synonymous and missense changes.
• Do not use missense_prediction, missense_inference, MissenseDataset, mutation_pred_clm, --organism_token, or --causal; those are newer unavailable public-release features.
• If a user asks specifically for synonymous-codon-aggregated missense scoring, explain that public v1 only provides the general ref/alt LLR. Do not silently substitute the two methods.