Skip to main content
Challenge

Build an Eukaryotic Coding Sequence Reading Frame Validator

Inspect multi-transcript FASTA packages from NCBI Datasets to detect reading frame shifts. Translate sequences using Biopython and structure discrepancy logs into FHIR Observation payloads.

Data ScienceHosted by Vera
Challenge brief

What you are building

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

Inspect multi-transcript FASTA packages from NCBI Datasets to detect reading frame shifts. Translate sequences using Biopython and structure discrepancy logs into FHIR Observation payloads.

Transcriptomics Specialist

Ingest CDS FASTA files downloaded via NCBI Datasets command-line utility.

Translate coding nucleotide sequences using Biopython standard genetic code translation tables.

Check for expected start codons, absence of internal stops, and exact nucleotide triplet multiples.

Format verification summaries into FHIR Molecular Observation entries with flagged discrepancy codes.

Terminal command-line utility with side-by-side terminal rendering of nucleotide triplets and amino acids.

Download human transcript packages for gene IDs 1, 2, and 3 using NCBI Datasets sandbox mocks. Starter examples below are synthetic. They are not downloaded records.

NCBI Datasets gene download Uses NCBI Datasets command 'datasets download gene gene-id 1 2 3 --include cds' to retrieve coding sequences.

ollama, biopython, pydantic-ai. Runs local open language models to generate explanations of translational discrepancies without external API costs. Local deployment of an instruction-tuned model such as Llama 3 8B via Ollama.

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 into peptide strings using Biopython Bio.Seq module.

Detects non-triplet CDS lengths and flags them with explicit frameshift indicators.

Exports valid FHIR R5 Observation resources containing codon length and frame evaluation.

```json [ { "input": { "cds_id": "CDS_001", "sequence": "ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG" }, "expected_output": { "cds_id": "CDS_001", "length_bp": 39, "is_triplet_multiple": true, "start_codon_valid": true, "internal_stops_count": 1, "validation_status": "flagged_internal_stop" } }, { "input": { "cds_id": "CDS_002", "sequence": "ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATA" }, "expected_output": { "cds_id": "CDS_002", "length_bp": 38, "is_triplet_multiple": false, "start_codon_valid": true, "internal_stops_count": 0, "validation_status": "frameshift_length_anomaly" } } ] ```

Research transcript analysis only; no clinical diagnostic determinations or genetic counseling advice. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Research transcript analysis only; no clinical diagnostic determinations or genetic counseling advice. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Translates CDS sequences into peptide strings using Biopython Bio.Seq module. Detects non-triplet CDS lengths and flags them with explicit frameshift indicators. Exports valid FHIR R5 Observation resources containing codon length and frame evaluation.

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

Ingest CDS FASTA files downloaded via NCBI Datasets command-line utility. Translate coding nucleotide sequences using Biopython standard genetic code translation tables. Check for expected start codons, absence of internal stops, and exact nucleotide triplet multiples. Format verification summaries into FHIR Molecular Observation entries with flagged discrepancy codes.

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 into peptide strings using Biopython Bio.Seq module. Detects non-triplet CDS lengths and flags them with explicit frameshift indicators. Exports valid FHIR R5 Observation resources containing codon length and frame evaluation.

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

Terminal command-line utility with side-by-side terminal rendering of nucleotide triplets and amino acids.

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

Research transcript analysis only; no clinical diagnostic determinations or genetic counseling advice.

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

  • Ingest CDS FASTA files downloaded via NCBI Datasets command-line utility.

  • Translate coding nucleotide sequences using Biopython standard genetic code translation tables.

  • Check for expected start codons, absence of internal stops, and exact nucleotide triplet multiples.

  • Format verification summaries into FHIR Molecular Observation entries with flagged discrepancy codes.

Resources and assets

Reference links and supporting material

Dataset notes

Download human transcript packages for gene IDs 1, 2, and 3 using NCBI Datasets sandbox mocks. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Ollama

Local LLM runtime for offline inference.

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest CDS FASTA files downloaded via NCBI Datasets command-line utility. Translate coding nucleotide seque...
  • Translates CDS sequences into peptide strings using Biopython Bio.Seq module. Detects non-triplet CDS lengt...
  • Terminal command-line utility with side-by-side terminal rendering of nucleotide triplets and amino acids.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

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

Policy Serving
  • Ollama · 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.

Frequently Asked Questions about Build an Eukaryotic Coding Sequence Reading Frame Validator