Study Discovery

Published

Jun 2026

  • ID: DAS-002
  • Type: Foundations
  • Audience: Omics Data Scientists, Bioinformaticians, and Research Teams
  • Theme: Finding Studies That Match Your Question

Public repositories contain millions of samples and thousands of studies spanning a wide range of biological questions, populations, technologies, and experimental designs. The challenge is rarely the lack of data. Instead, the challenge is identifying studies that are relevant, trustworthy, and suitable for a specific objective.

Study discovery is the process of transforming a research objective into a set of candidate studies that can support downstream analysis. Before any data are downloaded, researchers must determine which studies are most appropriate for their intended purpose.

Why Study Discovery Matters

Effective data acquisition begins with selecting the right studies.

For example, a project focused on constructing a healthy reference microbiome cannot rely on a simple keyword search alone. Researchers must determine:

  • Which studies contain healthy individuals?
  • Which samples are human?
  • Which body sites are relevant?
  • Which metadata fields are available?
  • Which studies satisfy project-specific criteria?

The quality of study discovery directly influences the quality of the resulting dataset.

From Question to Search Strategy

Every study discovery process begins with a clearly defined objective.

Code 
flowchart LR
A[Research Objective] --> B[Search Strategy]
B --> C[Study Discovery]
C --> D[Study Evaluation]

flowchart LR
A[Research Objective] --> B[Search Strategy]
B --> C[Study Discovery]
C --> D[Study Evaluation]

Defining Inclusion Criteria

Healthy Reference Microbiome Example

Include:

  • Human samples
  • Healthy individuals
  • Stool samples
  • Adequate metadata
  • Publicly available sequencing data

Defining Exclusion Criteria

Examples of exclusion criteria include:

  • Disease cohorts
  • Antibiotic-treated individuals
  • Animal samples
  • Missing phenotype information
  • Incomplete metadata
  • Poor documentation

Repository Search Interfaces

Repository Typical Use
NCBI Broad study discovery
GEO Functional genomics
ENA Sequence archives
MGnify Microbiome studies
GWAS Catalog Variant studies

Metadata-Driven Discovery

Relevant metadata may include:

  • Disease status
  • Age
  • Sex
  • Geography
  • Body site
  • Treatment status
  • Sequencing platform

Study Evaluation Checklist

  • Does the study address the target population?
  • Is metadata available and sufficiently detailed?
  • Are raw sequencing data available?
  • Is the sample size adequate?
  • Is study documentation complete?
  • Can the study support the intended analysis?

AlphaBiomics Example

Build a healthy reference gut microbiome.

Code 
flowchart TD
A[Healthy Reference Goal] --> B[Search Public Repositories]
B --> C[Identify Candidate Studies]
C --> D[Review Metadata]
D --> E[Apply Inclusion and Exclusion Criteria]
E --> F[Shortlist Studies]

flowchart TD
A[Healthy Reference Goal] --> B[Search Public Repositories]
B --> C[Identify Candidate Studies]
C --> D[Review Metadata]
D --> E[Apply Inclusion and Exclusion Criteria]
E --> F[Shortlist Studies]

Looking Ahead

In the next chapter, we examine accession systems and the relationships between BioProjects, BioSamples, Experiments, and Runs.