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Challenge

Build a Dual-Species Metagenomic Contamination Classifier

Examine mixed-read FASTA sequence repositories to detect microbial contamination in mammalian cell-line catalogs. Compute sequence complexity indices and generate quarantine manifests for cell-line biocurators.

Data ScienceHosted by Vera
Challenge brief

What you are building

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

Examine mixed-read FASTA sequence repositories to detect microbial contamination in mammalian cell-line catalogs. Compute sequence complexity indices and generate quarantine manifests for cell-line biocurators.

Genomics Core Facility Scientist

Load multi-sample FASTA sequence files from cell-line research gene package downloads.

Compute GC ratio distributions and pentamer frequencies using Biopython parsing modules.

Compare sequence composition against known mammalian and mycoplasma reference distributions.

Export a validated sequence purity index and alert biocurators to suspected microbial contamination.

Web console showing read distribution plots, GC-content shift metrics, and sample disposition actions.

Download public gene data packages with NCBI Datasets (--taxon rodent) and introduce synthetic microbial control fragments. Starter examples below are synthetic. They are not downloaded records.

Biopython Employ Biopython SeqIO readers and nucleotide counting functions to compute base composition metrics.

mlflow, biopython, numpy. mlflow logs assay quality parameters, GC-drift distributions, and contamination threshold runs over time. Local script execution with optional hosted LLM for generating biocurator narrative 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.

Calculates sequence GC percentages across all ingested FASTA reads via Biopython.

Detects bimodal GC distributions indicative of dual-species foreign contamination.

Logs assay quality parameters and metric artifacts into an MLflow experiment run.

Emits human-actionable quarantine notifications for samples exceeding 15% contamination.

```json [ { "input": { "run_id": "RUN-CELL-881", "host_species": "Mus musculus", "expected_gc_mean": 0.42, "reads": [ { "id": "R1", "seq": "GCGCGCGCGCGCGCGCGCGCGCGCGC" }, { "id": "R2", "seq": "ATATATATATATATATATATATATAT" }, { "id": "R3", "seq": "ATCGATCGATCGATCGATCGATCGAT" } ] }, "expected_output": { "run_id": "RUN-CELL-881", "samples_analyzed": 3, "contamination_detected": true, "anomalous_read_ids": [ "R1" ], "recommendation": "flag_for_microbial_assay" } }, { "input": { "run_id": "RUN-CELL-882", "host_species": "Homo sapiens", "expected_gc_mean": 0.45, "reads": [ { "id": "R4", "seq": "ATCGATCGATCGATCGATCGATCGAT" } ] }, "expected_output": { "run_id": "RUN-CELL-882", "samples_analyzed": 1, "contamination_detected": false, "anomalous_read_ids": [], "recommendation": "pass" } } ] ```

Analytical quality assurance for cell line research; synthetic data only; no medical or in vitro diagnostic claims. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Analytical quality assurance for cell line research; synthetic data only; no medical or in vitro diagnostic claims. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Calculates sequence GC percentages across all ingested FASTA reads via Biopython. Detects bimodal GC distributions indicative of dual-species foreign contamination. Logs assay quality parameters and metric artifacts into an MLflow experiment run. Emits human-actionable quarantine notifications for samples exceeding 15% contamination.

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 multi-sample FASTA sequence files from cell-line research gene package downloads. Compute GC ratio distributions and pentamer frequencies using Biopython parsing modules. Compare sequence composition against known mammalian and mycoplasma reference distributions. Export a validated sequence purity index and alert biocurators to suspected microbial contamination.

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

Calculates sequence GC percentages across all ingested FASTA reads via Biopython. Detects bimodal GC distributions indicative of dual-species foreign contamination. Logs assay quality parameters and metric artifacts into an MLflow experiment run. Emits human-actionable quarantine notifications for samples exceeding 15% contamination.

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

Web console showing read distribution plots, GC-content shift metrics, and sample disposition actions.

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

Analytical quality assurance for cell line research; synthetic data only; no medical or in vitro diagnostic 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 multi-sample FASTA sequence files from cell-line research gene package downloads.

  • Compute GC ratio distributions and pentamer frequencies using Biopython parsing modules.

  • Compare sequence composition against known mammalian and mycoplasma reference distributions.

  • Export a validated sequence purity index and alert biocurators to suspected microbial contamination.

Resources and assets

Reference links and supporting material

Dataset notes

Download public gene data packages with NCBI Datasets (--taxon rodent) and introduce synthetic microbial control fragments. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

MLflow

MLOps

Evaluated output

No output checks are configured.

Evaluation checks
  • Load multi-sample FASTA sequence files from cell-line research gene package downloads. Compute GC ratio dis...
  • Calculates sequence GC percentages across all ingested FASTA reads via Biopython. Detects bimodal GC distri...
  • Web console showing read distribution plots, GC-content shift metrics, and sample disposition actions.
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

Action Space
  • MLflow · 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 a Dual-Species Metagenomic Contamination Classifier