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GP-BT-1EKR0B9BiotechnologyOpen for request

Antimicrobial-resistance genomic surveillance

A reproducible bioinformatics system for analysing public bacterial genomes and summarising antimicrobial-resistance genes, variants, lineages, and related sample patterns.

  • Nuxt
  • Vue
  • FastAPI
  • PostgreSQL
  • Python
  • Nextflow
  • Biopython
  • Docker

Project definition

Problem statement

Genomic resistance analysis depends on sequence quality, organism identity, reference databases, marker definitions, and metadata quality. The presence of a known gene does not always predict clinical resistance.

The engineering problem is to build a versioned pipeline that links every result to the sequence, database, threshold, and workflow version while separating genomic surveillance evidence from clinical interpretation.

Project objectives

  • Prepare public, de-identified bacterial sequence records with permitted epidemiological metadata.
  • Check read or assembly quality, contamination indicators, species assignment, and sample duplication.
  • Detect selected acquired resistance genes and documented resistance-associated variants.
  • Compare AMR profiles, sequence types, relatedness, time, and broad geography.
  • Record database versions, thresholds, provenance, quality exclusions, and limitations.

System design

System modules

01

Sample registry

Stores accession, organism, source category, broad place and time, sequence files, licence, and provenance.

02

Quality-control pipeline

Checks reads or assemblies, coverage proxies, contamination, species identity, and duplicate samples.

03

AMR analyser

Runs selected resistance-gene and variant databases under fixed identity, coverage, and quality thresholds.

04

Relatedness module

Creates selected typing, distance, phylogenetic, and cluster summaries with recorded methods.

05

Surveillance dashboard

Shows quality, AMR profiles, lineages, trees, trends, broad maps, filters, and complete workflow provenance.

Methodology

System workflow

  1. 01
    Register public data

    The student imports permitted accessions and de-identified metadata into a fixed analysis set.

  2. 02
    Run quality checks

    Low-quality, contaminated, mismatched, or duplicate samples are flagged under documented rules.

  3. 03
    Detect markers

    Accepted sequences are compared with versioned AMR gene and variant references.

  4. 04
    Compare samples

    Typing and relatedness methods group samples and link patterns to time and broad source metadata.

  5. 05
    Review result

    The dashboard exposes exclusions, database versions, thresholds, evidence, and uncertainty for every summary.

Demonstration scenario

A prepared set of public bacterial genomes from two periods runs through quality control. The dashboard excludes one contaminated assembly, displays detected AMR genes and variants, and shows a small related group. Selecting a marker reveals its database version, sequence evidence, thresholds, and sample provenance.

Engineering

Technical architecture

Web application
Nuxt and Vue for sample sets, workflow status, AMR profiles, trees, trends, maps, and exports.
Bioinformatics API
FastAPI for sample metadata, controlled workflow jobs, results, provenance, and summaries.
Data layer
PostgreSQL for accessions, sample metadata, quality results, markers, types, distances, and workflow versions.
Workflow engine
Nextflow and containerised quality, taxonomy, AMR, typing, alignment, and phylogenetic tools.
Analysis layer
Python and Biopython for result normalisation, validation, summary metrics, and visualisation data.

Testing

Evaluation

Evaluation measures

  • Recovery of known markers in prepared positive and negative controls
  • Agreement with curated public reference results under matched database versions
  • Quality-filter correctness for prepared read and assembly cases
  • Reproducibility across repeated runs and computing environments
  • Sensitivity of summaries to identity, coverage, quality, and clustering thresholds
  • Workflow completion time and resource use across sample counts and sequence sizes

System boundaries

  • Only public, de-identified, synthetic, or explicitly permitted genomic data is included.
  • The prototype is for research and surveillance education and does not diagnose infection or recommend treatment.
  • Genomic markers do not replace phenotypic susceptibility testing or clinical interpretation.
  • Any apparent cluster or geographic pattern requires epidemiological investigation and independent confirmation.

Included

  1. 01Sequence, sample, workflow, and result interface
  2. 02Quality, resistance-marker, lineage, and comparison pipelines
  3. 03AMR profile, tree, map, trend, and provenance visualisations
  4. 04Prepared public bacterial sequences, metadata, and results
  5. 05Complete source code in a private GitHub repository
  6. 06Complete project documentation with synopsis, abstract, methodology, bioinformatics workflow diagrams, analysis results, screenshots, and conclusion
  7. 07Setup and usage guide

Project record

No buyer information is collected on this page.

Permanent project ID
GP-BT-1EKR0B9
Catalogued
21 Aug 2026
Completed
Pending
Verified
Pending
Demonstration
Added when ready

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.