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Related Experiment Video

Updated: Jun 20, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Towards large-scale sample annotation in gene expression repositories.

Erik Pitzer¹, Ronilda Lacson, Christian Hinske

¹Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA. erik.pitzer@fh-hagenberg.at

BMC Bioinformatics

|September 19, 2009

Summary

Researchers can now efficiently aggregate and analyze large biomedical datasets. A new software tool enables consistent sample annotation, unlocking previously inaccessible research data for translational studies.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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Last Updated: Jun 20, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Biomedical Informatics
Translational Research
Data Science

Background:

Biomedical research data aggregation is crucial for translational research.
Inconsistent sample annotations hinder data analysis and interpretation.
Key details like tissue type, cell line, and subject characteristics are often missing.

Purpose of the Study:

To develop a flexible software tool for efficient sample annotation.
To address the challenge of inconsistent and missing data in research repositories.

Main Methods:

Creation of a flexible software tool for data annotation.
Utilization of a controlled vocabulary for standardized descriptions.
Application of the tool to over 12,500 biological samples.

Main Results:

Successful development and implementation of a novel annotation tool.
Annotation of a large cohort exceeding 12,500 samples.
Demonstration of the tool's capability in managing diverse sample data.

Conclusions:

Tool-based re-annotation significantly enhances data accessibility.
Enables new possibilities for large-scale data interpretation and analysis.
Overcomes limitations posed by large, seemingly poorly annotated datasets.