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DFLAT: functional annotation for human development

Heather C Wick1*, Harold Drabkin2, Huy Ngu1, Michael Sackman1, Craig Fournier13, Jessica Haggett1, Judith A Blake2, Diana W Bianchi34 and Donna K Slonim13*

Author Affiliations

1 Department of Computer Science, Tufts University, 155 College Ave, Medford, MA 02155, USA

2 Bioinformatics and Computational Biology, The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA

3 Tufts University School of Medicine, 145 Harrison Ave, Boston, MA 02111, USA

4 Mother Infant Research Institute, Tufts Medical Center, 800 Washington St, Box 394, Boston, MA 02111, USA

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BMC Bioinformatics 2014, 15:45  doi:10.1186/1471-2105-15-45

Published: 7 February 2014

Abstract

Background

Recent increases in genomic studies of the developing human fetus and neonate have led to a need for widespread characterization of the functional roles of genes at different developmental stages. The Gene Ontology (GO), a valuable and widely-used resource for characterizing gene function, offers perhaps the most suitable functional annotation system for this purpose. However, due in part to the difficulty of studying molecular genetic effects in humans, even the current collection of comprehensive GO annotations for human genes and gene products often lacks adequate developmental context for scientists wishing to study gene function in the human fetus.

Description

The Developmental FunctionaL Annotation at Tufts (DFLAT) project aims to improve the quality of analyses of fetal gene expression and regulation by curating human fetal gene functions using both manual and semi-automated GO procedures. Eligible annotations are then contributed to the GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides valuable information about developmental genomics. A collection of gene sets (genes implicated in the same function or biological process), made by combining existing GO annotations with the 13,344 new DFLAT annotations, is available for use in novel analyses. Gene set analyses of expression in several data sets, including amniotic fluid RNA from fetuses with trisomies 21 and 18, umbilical cord blood, and blood from newborns with bronchopulmonary dysplasia, were conducted both with and without the DFLAT annotation.

Conclusions

Functional analysis of expression data using the DFLAT annotation increases the number of implicated gene sets, reflecting the DFLAT’s improved representation of current knowledge. Blinded literature review supports the validity of newly significant findings obtained with the DFLAT annotations. Newly implicated significant gene sets also suggest specific hypotheses for future research. Overall, the DFLAT project contributes new functional annotation and gene sets likely to enhance our ability to interpret genomic studies of human fetal and neonatal development.

Keywords:
Human development; Functional annotation; Databases; Gene function; Fetal; Neonatal; Gene set analysis