Build a Research Specimen Biopython Quality Control and Terminology Mapper
Evaluate sequencing run quality metrics from FASTA files and align specimen metadata to standard terminology. Structure results into FHIR Specimen and ResearchStudy observation entities.
What you are building
The core problem, expected build, and operating context for this challenge.
Evaluate sequencing run quality metrics from FASTA files and align specimen metadata to standard terminology. Structure results into FHIR Specimen and ResearchStudy observation entities.
Laboratory Operations Specialist
Read raw specimen FASTA sequences using Biopython SeqIO.
Calculate GC percentage and ambiguity counts for each sample record.
Validate sample metadata against FHIR Foundation administration resource models.
Format audit summaries into structured QC reports for human sign-off.
Web dashboard showing specimen batch QC tables with downloadable FHIR JSON manifests.
Generate synthetic FASTA files with varied nucleotide compositions and simulated specimen identifiers. Starter examples below are synthetic. They are not downloaded records.
Biopython Leverages Biopython's core SeqIO module for parsing FASTA files and calculating sequence composition stats.
mastra-ai, biopython, fastapi. Mastra AI provides declarative workflow orchestration to link parsing, terminology matching, and report generation. Fast hosted model or local Ollama instance for contextual anomaly explanations in QC summaries.
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.
Biopython SeqIO correctly calculates GC content and flags degenerate base thresholds.
Generates valid FHIR Specimen JSON representations with accurate status values.
Includes a human review staging queue for rejected specimens.
```json [ { "input": { "specimen_id": "SPEC-001", "sequence_header": ">sample_01 human_control", "sequence": "GCGCGCGCATATATATGCGC" }, "expected_output": { "specimen_id": "SPEC-001", "length": 20, "gc_content_pct": 60, "qc_status": "PASS", "fhir_resource": "Specimen" } }, { "input": { "specimen_id": "SPEC-002", "sequence_header": ">sample_02 degraded_isolate", "sequence": "NNNNNNCGATNNN" }, "expected_output": { "specimen_id": "SPEC-002", "length": 13, "gc_content_pct": 30.77, "qc_status": "FAIL_HIGH_AMBIGUITY", "fhir_resource": "Specimen" } } ] ```
Confined to pre-analytical laboratory assay quality checks using deidentified research identifiers. Use mock external writes. Require human review before any real deployment.
Participation guide
Confined to pre-analytical laboratory assay quality checks using deidentified research identifiers. Use mock external writes and human review.
How work is evaluated
Biopython SeqIO correctly calculates GC content and flags degenerate base thresholds. Generates valid FHIR Specimen JSON representations with accurate status values. Includes a human review staging queue for rejected specimens.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
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.
Complete workflow
Read raw specimen FASTA sequences using Biopython SeqIO. Calculate GC percentage and ambiguity counts for each sample record. Validate sample metadata against FHIR Foundation administration resource models. Format audit summaries into structured QC reports for human sign-off.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
Biopython SeqIO correctly calculates GC content and flags degenerate base thresholds. Generates valid FHIR Specimen JSON representations with accurate status values. Includes a human review staging queue for rejected specimens.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Web dashboard showing specimen batch QC tables with downloadable FHIR JSON manifests.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
Confined to pre-analytical laboratory assay quality checks using deidentified research identifiers.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Read raw specimen FASTA sequences using Biopython SeqIO.
Calculate GC percentage and ambiguity counts for each sample record.
Validate sample metadata against FHIR Foundation administration resource models.
Format audit summaries into structured QC reports for human sign-off.
Reference links and supporting material
Generate synthetic FASTA files with varied nucleotide compositions and simulated specimen identifiers. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Mastra AI
TypeScript agent framework.
Evaluated output
No output checks are configured.
- Read raw specimen FASTA sequences using Biopython SeqIO. Calculate GC percentage and ambiguity counts for e...
- Biopython SeqIO correctly calculates GC content and flags degenerate base thresholds. Generates valid FHIR...
- Web dashboard showing specimen batch QC tables with downloadable FHIR JSON manifests.
- 2 public reference cases
- 2 configured tools
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
- Mastra AI · Optional
- Mastra AI · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.