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Challenge

Build a Biopython Multiple Alignment Outlier Detector with Qdrant

Compute pairwise distance vectors across homologous transcripts and index representations into Qdrant. Flag outlier sequences and output FHIR DiagnosticReport payloads for biocuration teams.

Data ScienceHosted by Vera
Challenge brief

What you are building

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

Compute pairwise distance vectors across homologous transcripts and index representations into Qdrant. Flag outlier sequences and output FHIR DiagnosticReport payloads for biocuration teams.

Phylogenomics Analyst

Align homologous transcript sets using Biopython multiple sequence alignment utilities.

Extract k-mer frequency vectors and alignment distance scores for each sequence record.

Index sequence feature embeddings into Qdrant vector collections to calculate centroid distances.

Flag distance outliers and assemble FHIR DiagnosticReport payloads highlighting anomalous accessions.

Interactive dashboard showing scatter-plot cluster distributions and downloadable outlier reports.

Fetch synthetic eukaryotic gene packages for ACRV1 and homologous vertebrate loci from mock data stores. Starter examples below are synthetic. They are not downloaded records.

Biopython Applies Biopython multiple alignment parsing and sequence distance calculation routines documented in README.

qdrant, biopython, fastapi. Stores high-dimensional k-mer vectors and performs cosine distance queries to detect structural sequence outliers. Fast remote LLM for summarizing cluster anomalies and framing curator review notes.

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.

Computes k-mer frequency vectors from Biopython alignment sequences.

Stores and queries vector embeddings in a local or remote Qdrant collection.

Identifies sequences that deviate beyond two standard deviations from cluster centroids.

```json [ { "input": { "dataset_id": "HOM_GRP_10", "sequences": [ { "id": "TRANS_A", "seq": "ATGCGATCGATCGATC" }, { "id": "TRANS_B", "seq": "ATGCGATCGATCGATC" }, { "id": "TRANS_OUTLIER", "seq": "TTTTTTTTTTTTTTTT" } ] }, "expected_output": { "dataset_id": "HOM_GRP_10", "total_sequences": 3, "outliers_detected": [ "TRANS_OUTLIER" ], "cluster_centroid_distance": 0.89, "fhir_report_generated": true } }, { "input": { "dataset_id": "HOM_GRP_11", "sequences": [ { "id": "TRANS_1", "seq": "ATGGCTAGCTAG" }, { "id": "TRANS_2", "seq": "ATGGCTAGCTAG" } ] }, "expected_output": { "dataset_id": "HOM_GRP_11", "total_sequences": 2, "outliers_detected": [], "cluster_centroid_distance": 0, "fhir_report_generated": true } } ] ```

Research-only sequence clustering; completely separate from patient diagnostic testing. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Research-only sequence clustering; completely separate from patient diagnostic testing. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Computes k-mer frequency vectors from Biopython alignment sequences. Stores and queries vector embeddings in a local or remote Qdrant collection. Identifies sequences that deviate beyond two standard deviations from cluster centroids.

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

Align homologous transcript sets using Biopython multiple sequence alignment utilities. Extract k-mer frequency vectors and alignment distance scores for each sequence record. Index sequence feature embeddings into Qdrant vector collections to calculate centroid distances. Flag distance outliers and assemble FHIR DiagnosticReport payloads highlighting anomalous accessions.

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

Computes k-mer frequency vectors from Biopython alignment sequences. Stores and queries vector embeddings in a local or remote Qdrant collection. Identifies sequences that deviate beyond two standard deviations from cluster centroids.

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

Interactive dashboard showing scatter-plot cluster distributions and downloadable outlier reports.

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-only sequence clustering; completely separate from patient diagnostic testing.

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

  • Align homologous transcript sets using Biopython multiple sequence alignment utilities.

  • Extract k-mer frequency vectors and alignment distance scores for each sequence record.

  • Index sequence feature embeddings into Qdrant vector collections to calculate centroid distances.

  • Flag distance outliers and assemble FHIR DiagnosticReport payloads highlighting anomalous accessions.

Resources and assets

Reference links and supporting material

Dataset notes

Fetch synthetic eukaryotic gene packages for ACRV1 and homologous vertebrate loci from mock data stores. 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
  • Align homologous transcript sets using Biopython multiple sequence alignment utilities. Extract k-mer frequ...
  • Computes k-mer frequency vectors from Biopython alignment sequences. Stores and queries vector embeddings i...
  • Interactive dashboard showing scatter-plot cluster distributions and downloadable outlier reports.
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 Biopython Multiple Alignment Outlier Detector with Qdrant