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Challenge

Build a Multi-Transcript Coding Frame and Peptide Translation Auditor

Inspect coding sequence coordinates across multi-transcript gene packages to identify translational frame shifts. Generate structured FHIR Observation payloads tracking open reading frame integrity.

Data ScienceHosted by Vera
Challenge brief

What you are building

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

Inspect coding sequence coordinates across multi-transcript gene packages to identify translational frame shifts. Generate structured FHIR Observation payloads tracking open reading frame integrity.

Functional Genomics Biocurator

Load CDS and protein sequences from NCBI gene packages using Biopython.

Translate nucleotide CDS records across all three reading frames using the standard translation table.

Identify reading frame alignments that produce exact peptide matches against NCBI RefSeq protein records.

Generate FHIR Observation resources documenting reading frame validity and detected stop anomalies.

FastAPI backend serving JSON inspection endpoints and an HTML table displaying frame translation alignments.

Download human BRCA1 gene packages using NCBI Datasets CLI --include cds,protein into local sandbox folders. Starter examples below are synthetic. They are not downloaded records.

NCBI Datasets gene download Processes CDS and protein sequences retrieved via NCBI Datasets --include cds,protein parameters.

pydantic-ai, biopython, fastapi. Pydantic AI guarantees type constraints and schema enforcement for complex translation and reading frame models. Lightweight API or local model to generate structured explanations of frame-shift causes.

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.

Translates CDS sequences using Biopython Seq.translate and compares with target protein sequences.

Identifies reading frame offset (frame 0, +1, or +2) matching the documented protein product.

Outputs compliant FHIR R5 Observation records with appropriate status codes and valueCodings.

```json [ { "input": { "transcript_id": "NM_0001", "cds_seq": "ATGGCCAACGTATAG", "expected_protein": "MANV" }, "expected_output": { "frame_offset": 0, "exact_match": true, "fhir_observation": { "resourceType": "Observation", "status": "final", "code": { "coding": [ { "system": "http://hl7.org/fhir/sid/ncbi-gene", "code": "NM_0001" } ] }, "valueString": "In-frame translation confirmed with matching protein accession" } } }, { "input": { "transcript_id": "NM_0002", "cds_seq": "TGGCCAACGTATAG", "expected_protein": "MANV" }, "expected_output": { "frame_offset": null, "exact_match": false, "fhir_observation": { "resourceType": "Observation", "status": "preliminary", "code": { "coding": [ { "system": "http://hl7.org/fhir/sid/ncbi-gene", "code": "NM_0002" } ] }, "valueString": "Frame shift or missing start codon: no reading frame matched MANV" } } } ] ```

Uses open-access reference transcript sequences without individual patient exposure or clinical claims. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Uses open-access reference transcript sequences without individual patient exposure or clinical claims. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Translates CDS sequences using Biopython Seq.translate and compares with target protein sequences. Identifies reading frame offset (frame 0, +1, or +2) matching the documented protein product. Outputs compliant FHIR R5 Observation records with appropriate status codes and valueCodings.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

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
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Load CDS and protein sequences from NCBI gene packages using Biopython. Translate nucleotide CDS records across all three reading frames using the standard translation table. Identify reading frame alignments that produce exact peptide matches against NCBI RefSeq protein records. Generate FHIR Observation resources documenting reading frame validity and detected stop anomalies.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Translates CDS sequences using Biopython Seq.translate and compares with target protein sequences. Identifies reading frame offset (frame 0, +1, or +2) matching the documented protein product. Outputs compliant FHIR R5 Observation records with appropriate status codes and valueCodings.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

FastAPI backend serving JSON inspection endpoints and an HTML table displaying frame translation alignments.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Uses open-access reference transcript sequences without individual patient exposure or clinical claims.

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

  • Load CDS and protein sequences from NCBI gene packages using Biopython.

  • Translate nucleotide CDS records across all three reading frames using the standard translation table.

  • Identify reading frame alignments that produce exact peptide matches against NCBI RefSeq protein records.

  • Generate FHIR Observation resources documenting reading frame validity and detected stop anomalies.

Resources and assets

Reference links and supporting material

Dataset notes

Download human BRCA1 gene packages using NCBI Datasets CLI `--include cds,protein` into local sandbox folders. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Pydantic AI

Typed Python agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • Load CDS and protein sequences from NCBI gene packages using Biopython. Translate nucleotide CDS records ac...
  • Translates CDS sequences using Biopython Seq.translate and compares with target protein sequences. Identifi...
  • FastAPI backend serving JSON inspection endpoints and an HTML table displaying frame translation alignments.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 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

Action Space
  • Pydantic AI · Optional
Orchestration
  • Pydantic 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.

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