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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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DFLAT: functional annotation for human development.

Heather C Wick¹, Harold Drabkin, Huy Ngu

¹Department of Computer Science, Tufts University, 155 College Ave, Medford, MA 02155, USA. hwick@cs.tufts.edu.

BMC Bioinformatics

|February 11, 2014

Summary

This summary is machine-generated.

The Developmental Functional Annotation at Tufts (DFLAT) project enhances gene function annotation for human fetal development. This improves genomic study interpretation and identifies new research hypotheses in developmental genomics.

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Area of Science:

Genomics
Developmental Biology
Bioinformatics

Background:

Genomic studies of fetal and neonatal development are increasing.
Gene Ontology (GO) is a key resource for gene function characterization.
Existing GO annotations lack sufficient developmental context for human fetal studies.

Purpose of the Study:

To improve the quality of analyses of fetal gene expression and regulation.
To curate human fetal gene functions using manual and semi-automated GO procedures.
To contribute enhanced functional annotation to the GO database.

Main Methods:

The Developmental Functional Annotation at Tufts (DFLAT) project curated 13,344 new human fetal gene functions.
New annotations were integrated with existing GO data to create gene sets.
Gene set analyses were performed on various human developmental datasets with and without DFLAT annotations.

Main Results:

DFLAT annotations significantly increased the number of implicated gene sets in expression data analyses.
Newly identified gene sets suggest specific hypotheses for future research.
Blinded literature review validated the significance of findings derived from DFLAT annotations.

Conclusions:

The DFLAT project provides valuable functional annotation and gene sets for human fetal and neonatal development.
Enhanced annotation improves the interpretation of genomic studies.
DFLAT contributes to a better understanding of developmental genomics.