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Challenge

Implement a PCR Artifact Screening Validator with Pydantic AI

PCR leaping artifacts cause high false-positive variant calls during high-throughput bacterial gene screening. Build a type-safe PCR sequence validation agent using Pydantic AI to filter synthetic sequence chimeras and return structured validation models with zero schema validation errors.

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Challenge brief

What you are building

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

Type-safe sequence artifact detection pipeline constructed using Pydantic AI framework.

Delivery guide

How work is evaluated

Evaluation

Evaluates chimera artifact detection accuracy and output Pydantic schema validation adherence.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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

schema_conformance_check

Verifies output conforms strictly to Pydantic schema.

Binary check

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

Dimension 2

artifact_detection_accuracy

Accuracy in distinguishing real variants from PCR leaping artifacts. • 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

  • Build type-safe agent workflows using Pydantic AI.

  • Define strict Pydantic model schemas for PCR amplification artifact validation.

  • Identify template jumping and PCR chimera signatures in bacterial gene sequence reads.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 50 synthetic bacterial PCR amplicon sequences with simulated template switching artifacts.

Challenge setup

Evaluates chimera artifact detection accuracy and output Pydantic schema validation adherence.

Challenge input

JSON containing read_id, sequence_string, and target_gene

Pydantic AI

Framework specified for type-safe structured output agent validation.

Evaluated output

JSON compliant with PCRValidationResult model (is_artifact, chimera_junction_pos, confidence)

Evaluation checks
  • Verifies output conforms strictly to Pydantic schema.
  • Accuracy in distinguishing real variants from PCR leaping artifacts. • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: SeqQC-Bench
  • Artifact Detection Accuracy target: 0.92
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Llama 3.2 · Optional
  • ONNX Runtime · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • schema_conformance_check · Weight 1
  • artifact_detection_accuracy · Weight 1

Metrics

  • artifact_detection_accuracy · Target 0.92

Benchmarks

  • SeqQC-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing read_id, sequence_string, and target_gene

    Output: JSON compliant with PCRValidationResult model (is_artifact, chimera_junction_pos, confidence)

    Execution harness: Python

Evaluation tools

  • Pydantic AI · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

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