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Challenge

Missense Variant Pathogenicity Triage Agent with OpenAI Agents SDK

Interpreting missense variants requires synthesizing clinical literature and structural pathogenicity predictions. Build a multi-step variant triage agent using OpenAI Agents SDK and DeepSeek R1 to classify genomic variants and generate validated ACMG criteria classifications for patient diagnostic pipelines.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an autonomous variant triage agent using OpenAI Agents SDK backed by DeepSeek R1 reasoning to evaluate genomic missense functional impacts.

Delivery guide

How work is evaluated

Evaluation

Evaluates agent correctness in mapping genomic variants to ACMG criteria and final pathogenicity classifications.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

ACMG Code Format Validation

Checks that returned ACMG criteria conform to standard terminology.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Classification Accuracy

Accuracy of pathogenic/benign assignments against ClinVar ground truth. • target: 0.92 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement multi-turn genomic agent workflows using OpenAI Agents SDK.

  • Incorporate DeepSeek R1 chain-of-thought capabilities for ACMG evidence criteria synthesis.

  • Integrate missense functional prediction scores (e.g., REVEL, AlphaMissense) into decision tool calls.

  • Produce standardized VCF annotation and clinical pathogenicity outputs.

Resources and assets

Reference links and supporting material

Dataset notes

Curated set of 500 missense variants with annotated REVEL scores, gnomAD allele frequencies, and ClinVar ground truth classifications.

How this agent runs

Evaluates agent correctness in mapping genomic variants to ACMG criteria and final pathogenicity classifications.

Challenge input

JSON with variant chr, position, ref, alt, gene, and functional scores

OpenAI Agents SDK

Provides function-calling agent orchestration primitives.

DeepSeek R1

Delivers strong chain-of-thought clinical evidence synthesis.

Evaluated output

JSON with classification, applied_acmg_codes, and reasoning_summary

Checks for
  • Checks that returned ACMG criteria conform to standard terminology.
  • Accuracy of pathogenic/benign assignments against ClinVar ground truth. • target: 0.92 • range: 0-1
Proof of success
  • Benchmark: ClinVar-Eval
  • Classification Accuracy target: 0.92
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • BLACKBOX AI · Optional
Policy Serving
  • DeepSeek R1 · Optional

Evaluation contract

  • ACMG Code Format Validation · Weight 1
  • Classification Accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Missense Variant Pathogenicity Triage Agent with OpenAI Agents SDK