A 360-degree framework for pathogen genomics

Build, map, and assess workforce capability across the full pathogen genomics pipeline.

The Pathogen Genomics Competency Framework supports educators, curriculum developers, workforce planners, and learners by organising pathogen genomics into a practical structure that spans laboratory science, bioinformatics, epidemiology, clinical interpretation, and public health application.

Explore the domains Implement the model

Domains: 10
Topics: 60
Competencies: 360
Bloom levels: 6

Why it matters

Pathogen genomics needs more than sequencing expertise.

Effective implementation depends on connected capabilities across sample handling, laboratory workflows, genomic analysis, data interpretation, ethics, communication, training, and programme management.

For curriculum design

Map learning outcomes, identify gaps, and align teaching and assessment.

For workforce planning

Link competencies to roles, capability reviews, and staff development priorities.

For learners and teams

Support reflection, target-setting, and structured professional development.

At a glance

A framework designed for real-world use

The PGCF is intended as an evolving, flexible framework rather than a prescriptive checklist. It helps users select the most relevant domains and competencies for their setting, then apply them to curriculum review, training design, self-assessment, or workforce development.

Breadth across the pipeline

Covers the continuum from genomes and microbiology to sample preparation, data generation, analysis, interpretation, and decision-making.

Depth through structured progression

Each topic includes six Bloom-aligned competency statements, enabling progression from foundational understanding to advanced evaluation and creation.

Cross-cutting implementation skills

Includes ethics, communication, data science, learning and training, and capacity development to support adoption beyond technical workflows alone.

Framework structure

Ten domains covering technical, applied, and cross-cutting capability

The framework organises 60 topics into domains that can be used together or selected according to role, course, or institutional need.

Domain 1

Genomes and Microbiology

Foundational biology, pathogen diversity, microbiology concepts, and the scientific context needed to interpret genomic information.

Domain 2

Sample Preparation and Processing

Specimen collection, handling, pre-analytical considerations, and preparation steps that underpin reliable downstream analysis.

Domain 3

Sample Analysis and Data Generation

Sequencing technologies, laboratory workflows, quality control, and generation of usable pathogen genomics data.

Domain 4

Genomic Data Workflows, Analysis and Integration

Core bioinformatics workflows, processing pipelines, integration of multiple data types, and reproducible analytical practice.

Domain 5

Data Interpretation

Interpretation of genomic outputs, contextualisation with epidemiological evidence, and translation into meaningful conclusions.

Domain 6

Clinical and Public Health Decision-Making

Application of pathogen genomics to surveillance, outbreak response, infection control, and decision support in health systems.

Domain 7

Data Science

Data stewardship, management, visualisation, ethics, governance, and computational practices that support trustworthy use of data.

Domain 8

Integrated Skills

Communication, interdisciplinary collaboration, leadership, and professional capabilities needed to embed genomics into practice.

Domain 9

Learning and Training

Teaching, facilitation, mentoring, and support for developing learning pathways in pathogen genomics.

Domain 10

Capacity Development and Programme Management

Planning, implementation, evaluation, coordination, and long-term development of programmes that strengthen genomics capability at institutional or national level.

How to use it

From gap analysis to workforce planning

The user guide describes several flexible ways to apply the PGCF. Users can work at the level of topics, competencies, courses, job roles, or organisational capability planning.

Gap analysis

Review an existing curriculum or programme to see which topics are fully covered, partially covered, or missing.

Curriculum design

Select relevant domains and competencies, define learning outcomes, and match teaching and assessment methods to Bloom's levels.

Module mapping

Align course modules and learning outcomes directly to PGCF competencies for transparent coverage and progression.

Proficiency profiles

Use structured self or supervisor review to compare current and expected levels, then identify training priorities.

Who is it for?

The framework is especially useful for educators, training developers, curriculum designers, workforce planners, public health agencies, and learners working across pathogen genomics.

Laboratory scientists and biomedical scientists
Bioinformaticians and data specialists
Epidemiologists and public health teams
Clinical scientists, consultants, and researchers

Toolkit

Digital tools and supporting resources

The PGCF is supported by practical tools designed to make implementation easier across different contexts.

Competency app

Browse topics and competencies through the digital interface and support structured reflection.

Code repository

Access the source code, competency CSV file, requirements file, and implementation resources.

Framework website

A public-facing home for the framework, its description, and related implementation materials.

Supporting record

Reference material connected to the wider T3Connect work that informed development of the framework.

Implementation guide

How another institution can implement the same model

This example uses three main parts: GitHub to store the open source code, Streamlit to run the public web app, and Google Cloud Storage to keep submitted competency self-assessment responses in a private institutional bucket.

Plain-language explanation

The code is open so that others can reuse it, but the data and credentials are private. Participants use the Streamlit website, and when they submit their assessment, the app saves the response in a controlled Google Cloud bucket. Access is limited to authorised institutional staff.

What happens on submit

1. Participant's browser: The participant completes the self-assessment in the Streamlit website.
2. Streamlit app: The app turns the responses into a structured CSV file.
3. Streamlit secrets: The private Google Cloud credential is stored in the app settings, not in the public GitHub code and not in the participant's browser.
4. Google Cloud Storage: The response file is uploaded to a private bucket controlled by the institution.

Step 1

Copy or fork the GitHub repository

Start from the PGCF repository. It contains the main app file, the competency CSV file, and a requirements.txt file listing the Python packages needed by the app, including Streamlit, pandas, and Google Cloud Storage.

Step 2

Adapt the competency framework

Replace or edit the competency CSV file with the institution's own competency framework. The app reads the content from PGCF.csv, then displays the domains, topics, and levels in the Streamlit interface.

Step 3

Create a private Google Cloud Storage bucket

Create a Google Cloud project and a private bucket for assessment responses. For simpler access control, enable uniform bucket-level access so permissions are managed through IAM rather than object-level ACLs.

Google Cloud guide: uniform bucket-level access →

Step 4

Create a service account for the app

The Streamlit app needs a Google Cloud identity that can upload files to the bucket. For a write-only submission workflow, grant the service account the Storage Object Creator role, roles/storage.objectCreator, on the bucket.

Google Cloud guide: Cloud Storage IAM roles →

Step 5

Store credentials as secrets, not in GitHub

The service account credential should be stored in Streamlit secrets. It should not be committed to the repository. In Streamlit Community Cloud, add secrets through the app settings.

[gcp]
bucket_name = "your-private-bucket-name"
service_account_json = """{
  "type": "service_account",
  "project_id": "your-project-id",
  "...": "..."
}"""

Why this matters

GitHub contains the reusable recipe for the app. Streamlit secrets contain the private credential that allows the deployed app to write to the institution's bucket.

Streamlit guide: secrets management → Google Cloud guide: service account key practices →

Step 6

Deploy the app

In Streamlit Community Cloud, create an app from the GitHub repository, select the main Python file, add the required secrets in the app settings, and deploy.

Streamlit guide: deploy your app →

Step 7

Test the full submission process

Submit a test response, confirm that the file appears in the Google Cloud Storage bucket, and confirm that only authorised staff can access the stored responses.

Security and privacy

Points to include before an institution launches its own version

The public website should explain that the code is open, but the assessment data and cloud credentials are private. Each institution should also make local decisions about identity, consent, access, retention, and data governance.

Use IDs rather than names where possible

Names, job titles, and role descriptions can make a dataset personally identifiable. Participant IDs may be safer when individual names are not necessary.

Do not reveal bucket details to users

A public-facing confirmation message should say that the response was submitted securely. It should not show the bucket path or internal storage details.

Enable secret-protection practices in GitHub

Secret scanning and push protection help prevent credentials, tokens, and private keys from being accidentally committed to a repository.

GitHub guide: push protection →

Document data governance

Before launch, decide who owns the data, who can access it, how long responses are kept, whether names are collected, and what participants are told before they submit.

Google Cloud guide: default encryption at rest →

Avoid duplicate file names

Do not use only the participant's name or ID as the filename. Add a timestamp and a random ID so repeated submissions cannot overwrite each other.

from datetime import datetime, timezone
from uuid import uuid4

timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
file_name = f"responses/{timestamp}_{uuid4().hex}.csv"

upload_to_gcs_from_bytes(csv_bytes, file_name)
st.success("Your response has been submitted securely.")

Next step

Use the framework to support learning, planning, and implementation.

Start with the domains most relevant to your audience, map them to roles or learning outcomes, and use the digital tools to identify strengths and training needs.

Open the app Implementation guide Back to top