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DFLAT: functional annotation for human development.

Wick HC, Drabkin H, Ngu H, Sackman M, Fournier C, Haggett J, Blake JA, Bianchi DW, Slonim DK - BMC Bioinformatics (2014)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

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

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.

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Overview of sources of DFLAT annotation. On the left-hand side, neonatologists suggest keywords and developmental processes for manual curation of the literature, which proceeds according to the methodology of the Gene Ontology Consortium. Eligible annotations are submitted to the Gene Ontology and included in subsequent data releases. Others, valuable for our purposes, become part of the “GONE” collection. The right-hand side of the image depicts our procedure for deriving annotation from mouse orthologs. Mouse genes of interest are identified by having GO annotations in the “Developmental Process” subtree. For those genes for which MGI has identified unique human orthologs, all mouse annotations with the required evidence codes are mapped to the corresponding human gene.
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Figure 1: Overview of sources of DFLAT annotation. On the left-hand side, neonatologists suggest keywords and developmental processes for manual curation of the literature, which proceeds according to the methodology of the Gene Ontology Consortium. Eligible annotations are submitted to the Gene Ontology and included in subsequent data releases. Others, valuable for our purposes, become part of the “GONE” collection. The right-hand side of the image depicts our procedure for deriving annotation from mouse orthologs. Mouse genes of interest are identified by having GO annotations in the “Developmental Process” subtree. For those genes for which MGI has identified unique human orthologs, all mouse annotations with the required evidence codes are mapped to the corresponding human gene.

Mentions: In total, 12,798 unique orthologous annotations were derived from this approach. An overview of the different sources of data in DFLAT appears in Figure 1.


DFLAT: functional annotation for human development.

Wick HC, Drabkin H, Ngu H, Sackman M, Fournier C, Haggett J, Blake JA, Bianchi DW, Slonim DK - BMC Bioinformatics (2014)

Overview of sources of DFLAT annotation. On the left-hand side, neonatologists suggest keywords and developmental processes for manual curation of the literature, which proceeds according to the methodology of the Gene Ontology Consortium. Eligible annotations are submitted to the Gene Ontology and included in subsequent data releases. Others, valuable for our purposes, become part of the “GONE” collection. The right-hand side of the image depicts our procedure for deriving annotation from mouse orthologs. Mouse genes of interest are identified by having GO annotations in the “Developmental Process” subtree. For those genes for which MGI has identified unique human orthologs, all mouse annotations with the required evidence codes are mapped to the corresponding human gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928322&req=5

Figure 1: Overview of sources of DFLAT annotation. On the left-hand side, neonatologists suggest keywords and developmental processes for manual curation of the literature, which proceeds according to the methodology of the Gene Ontology Consortium. Eligible annotations are submitted to the Gene Ontology and included in subsequent data releases. Others, valuable for our purposes, become part of the “GONE” collection. The right-hand side of the image depicts our procedure for deriving annotation from mouse orthologs. Mouse genes of interest are identified by having GO annotations in the “Developmental Process” subtree. For those genes for which MGI has identified unique human orthologs, all mouse annotations with the required evidence codes are mapped to the corresponding human gene.
Mentions: In total, 12,798 unique orthologous annotations were derived from this approach. An overview of the different sources of data in DFLAT appears in Figure 1.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

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

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.

Show MeSH
Related in: MedlinePlus