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Construct a Tetraploid Wheat Pangenome Graph Query Pipeline with ODGI and Python

Tetraploid wheat pangenomes contain rich structural variations essential for crop disease resistance and yield optimization. Build a variation graph parsing pipeline using ODGI and Python to query structural variation loci across tetraploid wheat lines with 95% detection accuracy.

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What you are building

Pangenome variation graph analysis engine using ODGI command-line utilities and Python wrappers.

How work is evaluated

Evaluation

Evaluates structural variant extraction accuracy from pangenome variation graphs.

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

sv_type_check

Verifies valid structural variation type returned.

Binary check

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

Dimension 2

sv_detection_accuracy

Accuracy of structural variant classification against ground truth GFA paths. • target: 0.95 • 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

  • Parse Graphical Pangenome Format (GFA) files using ODGI (Optimized Dynamic Genome Interface).

  • Identify complex structural variant (SV) breakpoints across tetraploid wheat assemblies.

  • Extract path-divergence metrics to isolate traits associated with domestication.

Resources

Dataset notes

Synthetic GFA variation graph representing a 10Mb region across 10 tetraploid wheat cultivars.

Challenge setup

Evaluates structural variant extraction accuracy from pangenome variation graphs.

Challenge input

JSON containing locus_node_id and cultivar_path_name

ODGI

Engine for dynamic variation graph analysis and query.

Evaluated output

JSON object with sv_type, sv_length_bp, and presence_absence_state

Evaluation checks
  • Verifies valid structural variation type returned.
  • Accuracy of structural variant classification against ground truth GFA paths. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: PanGraph-Bench
  • Sv Detection Accuracy target: 0.95
  • 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
  • WizardLM-2 · Required
  • xAI · Optional
  • Airweave · Optional

Evaluation contract

  • sv_type_check · Weight 1
  • sv_detection_accuracy · Weight 1

Metrics

  • sv_detection_accuracy · Target 0.95

Benchmarks

  • PanGraph-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing locus_node_id and cultivar_path_name

    Output: JSON object with sv_type, sv_length_bp, and presence_absence_state

    Execution harness: Python

Evaluation tools

  • ODGI · pangenomics_toolkit

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Construct a Tetraploid Wheat Pangenome Graph Query Pipeline with ODGI and Python