Skip to main content
Challenge

Build a Vector-Based Sequence Catalog Cross-Contamination Screener

Convert sequence k-mer profiles into vector embeddings to detect non-target species fragments in public sequence submissions. Index reference taxa profiles in Qdrant and isolate anomalous sequence entries.

Data ScienceHosted by Vera
Challenge brief

What you are building

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

Convert sequence k-mer profiles into vector embeddings to detect non-target species fragments in public sequence submissions. Index reference taxa profiles in Qdrant and isolate anomalous sequence entries.

Sequence Repository Quality Manager

Read input FASTA files and decompose reads into standardized oligonucleotide k-mer frequency vectors.

Index canonical species reference profiles within Qdrant collection spaces.

Query uploaded sequence vectors against reference spaces to compute cosine similarity scores.

Produce an automated contamination triage manifest flagging outliers for biocurator inspection.

Vector similarity scatter workspace with nearest-neighbor taxon matches and manual quarantine actions.

Synthesize normalized FASTA records and obtain reference k-mers from public NCBI eukaryotic gene downloads. Starter examples below are synthetic. They are not downloaded records.

NCBI Datasets gene download Extract multi-species FASTA records using NCBI Datasets parameters (--taxon human, --include rna).

qdrant, biopython, numpy. qdrant enables vector indexing and similarity search over sequence k-mer embedding representations. Local Python vectorizer runtime without specialized neural weights.

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.

Transforms FASTA nucleotide strings into normalized k-mer frequency vectors.

Indexes reference vectors into Qdrant collection and queries query sequence distances.

Identifies alien sequences that show top similarity to non-target taxon profiles.

Provides human-in-the-loop review actions to override or confirm sequence quarantine.

```json [ { "input": { "collection_name": "taxa_vectors", "target_taxon": "human", "query_sequences": [ { "id": "SEQ_01", "vector": [ 0.95, 0.05, 0 ] }, { "id": "SEQ_02", "vector": [ 0.05, 0.9, 0.05 ] } ], "reference_centroids": { "human": [ 0.92, 0.08, 0 ], "bacterial_contaminant": [ 0.08, 0.88, 0.04 ] } }, "expected_output": { "target_taxon": "human", "results": [ { "id": "SEQ_01", "matched_taxon": "human", "confidence": 0.99, "status": "approved" }, { "id": "SEQ_02", "matched_taxon": "bacterial_contaminant", "confidence": 0.98, "status": "quarantined" } ] } }, { "input": { "collection_name": "taxa_vectors", "target_taxon": "human", "query_sequences": [ { "id": "SEQ_EMPTY", "vector": [ 0, 0, 0 ] } ], "reference_centroids": { "human": [ 0.92, 0.08, 0 ] } }, "expected_output": { "target_taxon": "human", "results": [ { "id": "SEQ_EMPTY", "matched_taxon": "unassigned", "confidence": 0, "status": "flagged_error" } ] } } ] ```

Bioinformatics sequence quality control tool; operates on public or synthetic DNA sequences only. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Bioinformatics sequence quality control tool; operates on public or synthetic DNA sequences only. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Transforms FASTA nucleotide strings into normalized k-mer frequency vectors. Indexes reference vectors into Qdrant collection and queries query sequence distances. Identifies alien sequences that show top similarity to non-target taxon profiles. Provides human-in-the-loop review actions to override or confirm sequence quarantine.

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

Read input FASTA files and decompose reads into standardized oligonucleotide k-mer frequency vectors. Index canonical species reference profiles within Qdrant collection spaces. Query uploaded sequence vectors against reference spaces to compute cosine similarity scores. Produce an automated contamination triage manifest flagging outliers for biocurator inspection.

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

Transforms FASTA nucleotide strings into normalized k-mer frequency vectors. Indexes reference vectors into Qdrant collection and queries query sequence distances. Identifies alien sequences that show top similarity to non-target taxon profiles. Provides human-in-the-loop review actions to override or confirm sequence quarantine.

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

Vector similarity scatter workspace with nearest-neighbor taxon matches and manual quarantine 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

Bioinformatics sequence quality control tool; operates on public or synthetic DNA sequences only.

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

  • Read input FASTA files and decompose reads into standardized oligonucleotide k-mer frequency vectors.

  • Index canonical species reference profiles within Qdrant collection spaces.

  • Query uploaded sequence vectors against reference spaces to compute cosine similarity scores.

  • Produce an automated contamination triage manifest flagging outliers for biocurator inspection.

Resources and assets

Reference links and supporting material

Dataset notes

Synthesize normalized FASTA records and obtain reference k-mers from public NCBI eukaryotic gene downloads. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Qdrant

Open-source vector database.

Evaluated output

No output checks are configured.

Evaluation checks
  • Read input FASTA files and decompose reads into standardized oligonucleotide k-mer frequency vectors. Index...
  • Transforms FASTA nucleotide strings into normalized k-mer frequency vectors. Indexes reference vectors into...
  • Vector similarity scatter workspace with nearest-neighbor taxon matches and manual quarantine actions.
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
  • Qdrant · Optional
Observation
  • Qdrant · 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 Vector-Based Sequence Catalog Cross-Contamination Screener