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Epigenomic Pioneer Factor Dynamics Classifier using Pydantic AI

Pioneer factors direct cohesin loading across chromatin, requiring rigid schema validation for epigenomic data pipelines. Build a type-safe chromatin dynamics analysis agent using Pydantic AI and scikit-bio to analyze ChIP-seq and ATAC-seq peak intersections. Achieve 98% validated structured output schema compliance.

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What you are building

Construct a type-safe epigenomic workflow agent using Pydantic AI to analyze pioneer factor chromatin positioning.

How work is evaluated

Evaluation

Evaluates schema validation success and peak overlap calculation accuracy.

Datasets

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

How submissions are scored

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

pydantic_schema_pass

Output strictly parses into defined Pydantic class without validation error.

Binary check

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

Dimension 2

schema_compliance_rate

Percentage of generated outputs meeting exact Pydantic schema validation • target: 0.98 • 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

  • Implement type-safe agent pipelines using Pydantic AI framework

  • Parse genomic BED and narrowPeak data using scikit-bio utilities

  • Enforce strict structure output schemas for epigenomic analysis results

  • Calculate peak overlap enrichment for pioneer factors and cohesin loading sites

Resources

Dataset notes

ChIP-seq and ATAC-seq peak data from Nature Genetics publication mapping pioneer factors (e.g. FOXA1) and cohesin (RAD21).

Challenge setup

Evaluates schema validation success and peak overlap calculation accuracy.

Challenge input

JSON containing peak genomic coordinates (chr, start, end, signalValue) for pioneer factor and cohesin.

Pydantic AI

Framework specified for structured, validated agent outputs.

scikit-bio

Python toolkit for genomic interval arithmetic and sequence analysis.

Evaluated output

JSON object matching Pydantic model with binding_category, overlap_bp, and statistically_significant boolean.

Evaluation checks
  • Output strictly parses into defined Pydantic class without validation error.
  • Percentage of generated outputs meeting exact Pydantic schema validation • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: Genomic Peak Annotation Schema Benchmark
  • Schema Compliance Rate target: 98%
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Merge · Optional
Policy Serving
  • Hunyuan · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • pydantic_schema_pass · Weight 1
  • schema_compliance_rate · Weight 1

Metrics

  • schema_compliance_rate · Target 0.98

Benchmarks

  • Genomic Peak Annotation Schema Benchmark
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing peak genomic coordinates (chr, start, end, signalValue) for pioneer factor and cohesin.

    Output: JSON object matching Pydantic model with binding_category, overlap_bp, and statistically_significant boolean.

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Type-safe Agent Framework
  • scikit-bio · Bioinformatics Utility Library

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Epigenomic Pioneer Factor Dynamics Classifier using Pydantic AI