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.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a type-safe epigenomic workflow agent using Pydantic AI to analyze pioneer factor chromatin positioning.
How work is evaluated
Evaluates schema validation success and peak overlap calculation accuracy.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
pydantic_schema_pass
Output strictly parses into defined Pydantic class without validation error.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
schema_compliance_rate
Percentage of generated outputs meeting exact Pydantic schema validation • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
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.
- 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
- Benchmark: Genomic Peak Annotation Schema Benchmark
- Schema Compliance Rate target: 98%
- 1 public reference case
- 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
- Pydantic AI · Required
- Merge · Optional
- Hunyuan · Optional
- 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
- 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.