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Challenge

Tetraploid Wheat Pangenome Breeding Marker Discovery Pipeline with vg and Python

Polyploid crop pangenomes capture missing structural variation vital for agricultural breeding. You will construct a pangenome graph workflow using variation graphs (vg toolkit) and Python to map polyploid wheat genomes, identifying trait-associated structural variants with 90% mapping recall.

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Challenge brief

What you are building

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

Build a variation graph pangenome pipeline with vg and Python to genotype domestication structural variants in polyploid wheat.

Delivery guide

How work is evaluated

Evaluation

Evaluates pangenome read mapping recall and structural variant genotyping accuracy.

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

genotype_format_check

Ensure genotype string conforms to standard VCF format (e.g. 0/1, 1/1)

Binary check

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

Dimension 2

mapping_recall

Read alignment recall across complex subgenome regions • target: 0.9 • 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

  • Construct pangenome reference graphs using vg (variation graph) toolkit

  • Map short and long sequencing reads against subgenome-resolved polyploid graphs

  • Call structural variant breeding markers underlying key agricultural traits

Resources and assets

Reference links and supporting material

Dataset notes

Tetraploid wheat pangenome dataset including 10 assembly graphs and simulated read sets.

Challenge setup

Evaluates pangenome read mapping recall and structural variant genotyping accuracy.

Challenge input

JSON object with read_file_path and marker_locus string

vg toolkit

Standard graph genomics software suite.

Evaluated output

JSON object with genotype string and mapping_quality_mean float

Evaluation checks
  • Ensure genotype string conforms to standard VCF format (e.g. 0/1, 1/1)
  • Read alignment recall across complex subgenome regions • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: PangenomeGraph-Bench
  • Mapping Recall target: 0.9
  • 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
  • HiddenLayer · Required
  • Composio · Optional
  • 01.AI · Optional

Evaluation contract

  • genotype_format_check · Weight 1
  • mapping_recall · Weight 1

Metrics

  • mapping_recall · Target 0.9

Benchmarks

  • PangenomeGraph-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with read_file_path and marker_locus string

    Output: JSON object with genotype string and mapping_quality_mean float

    Execution harness: Python

Evaluation tools

  • vg toolkit · Pangenome Graph Toolkit

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Tetraploid Wheat Pangenome Breeding Marker Discovery Pipeline with vg and Python