Research Architecture

AREF Research Tracks

Your research track is the operational core of your participation. It is not a single project — it is a structured, multi-year research program built around a defined theme and supported by student cohorts at multiple academic levels.

Track Architecture Ongoing Projects Research Publications
Track Architecture
Seven Core Components of a Research Track

A well-structured AREF Research Track contains the following components. You will be guided to develop and refine each during your onboarding.

1
Research Theme Title
A clear, concise title for your research direction that captures the thematic scope of your track.
2
Biological or Disease Focus
The primary scientific problem or question your track addresses — from pathogen genomics to cancer biology or population genetics.
3
Dataset Strategy
The public or institutional genomic datasets relevant to your theme, selected and structured for meaningful analytical use.
4
Analytical Framework
The computational and bioinformatics methodology underpinning your research outputs and student cohort work.
5
Student Layering Model
How undergraduate, MSc, and PhD students will contribute at different levels, building progressively on one another's work.
6
Publication Pathway
Your planned research outputs, target journals, and the structured timeline from dataset analysis to submission.
7
Long-Term Expansion Vision
How your track will grow, deepen, and generate new research questions across a 2-to-5-year horizon, including new student cohorts and sub-tracks.
Student Layering Model
Three-Tier Research Contribution Architecture

One of the most powerful aspects of the AREF model is that your track can accommodate students at multiple academic levels simultaneously, with each level contributing progressively to the overall research direction.

Undergraduate
Foundation Layer
Dataset explorer & question framer
Undergraduate students form the foundation of your research track. They explore the dataset, frame initial research questions, and conduct basic descriptive analyses under your direct supervision.
Output: Initial results, dataset familiarity, question definition
Master's (MSc)
Analytical Depth Layer
Comparative investigator
MSc students build on undergraduate work, conducting comparative or deeper investigations with more rigorous methodology. Their work often forms the basis for publication contributions within your track.
Output: Stronger methodology, comparative outputs, publication contribution
Doctoral (PhD)
Advanced Research Layer
Novel contributor & innovator
Doctoral students lead novel contributions and methodological innovation within the track. They are expected to generate publication-grade findings that shape the long-term direction of your research program.
Output: Publication-grade findings, long-term track direction
Track Lifecycle
How Your Research Track Progresses

Your research track is expected to move through the following stages during your allocation cycle and beyond.

1
Ideation & Theme
Define the core direction and align to relevant datasets
2
Track Design
Complete the Research Blueprint with student layering and output plan
3
Cohort Activation
Align students to the track and initiate their research projects
4
Data & Analysis
Dataset access, computational analysis, and interpretation
5
Manuscript
Structure findings into publication-ready outputs
6
Submission
Submit to target journals with AREF pathway guidance
7
Dissemination
Present at AREF showcases, conferences, and in the network
8
Expansion
Generate new questions, add cohorts, deepen the research direction
Research Domains
Thematic Areas Within the AREF Network

Research tracks in the AREF network are organised around the following thematic domains.

Pathogen Genomics
Whole-genome sequencing of bacteria, viruses and parasites. AMR profiling, phylogenomics, and genomic epidemiology of infectious disease.
Microbiome Research
16S rRNA and metagenomic analyses of gut, oral, soil, and environmental microbiomes. Diversity analysis and host-microbiome interactions.
Cancer Genomics
Somatic mutation landscapes, hereditary cancer risk variants, and transcriptomic profiling of African-ancestry cancer cohorts.
Computational Biology
GWAS, population genomics, pharmacogenomics, and structural variant analysis using public African-ancestry genomic reference datasets.