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 GO database and included in GO releases of human data. DFLAT has produced a considerable body of functional annotation that we demonstrate provides useful information about developmental genomics. A collection of gene units (genes implicated in the same function or biological process), made by combining existing GO annotations 174636-32-9 supplier with the 13,344 new DFLAT annotations, is usually available for use in novel analyses. Gene set analyses of expression in several data units, 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 quantity of implicated gene units, reflecting the DFLATs improved representation 174636-32-9 supplier 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. analyses of functional categories overrepresented in lists of individually-implicated genes, it has become commonplace to use pre-defined gene sets to identify the implicated pathways [11-15]. For example, a gene set implicated in the process of single strand break repair might consist of the genes and Even if none of these genes is itself upregulated in a set of phenotypically related samples, if all of the genes are upregulated, the consistency of those changes might indicate that the process is indeed upregulated in the phenotype. Such gene-set analysis methods can be highly effective, but only if the functional annotation used to create the gene sets is informative about the specific conditions being studied . There are 174636-32-9 supplier several sources of functional pathway annotation used for this purpose. The most frequently referenced annotation source is the Gene Ontology (GO) , a collaborative effort to standardize the functional annotation of genes and gene products using a controlled vocabulary of terms connected by relationships that result in directed, acyclic graphs. The application of this vocabulary allows broad inferences to be made based on the grouping of many isolated annotations. Community participation and shared standards encourage consistent annotation across a wide range of species. GO annotations, linked to their supporting evidence in the primary literature, are publicly available and broadly relevant to a range of fields. Although not initially designed explicitly for this purpose, annotation from the GO is often used for gene set analysis [11,18-20]. The Gene Ontologys framework for representing developmental processes is quite detailed [21,22]. However, necessarily, much of the human genetic information in the GO database is derived from research conducted on adult subjects or in cultured cells. Other annotations linking genes to human developmental processes are derived from studies of embryonic development in invertebrate model organisms such as or Many of these genes do indeed have human orthologs with similar functions, especially in the realms of cell polarity, neurological development, and immunity . However, other human developmental processes, particularly those crucial in later stages of development, are not as well modeled in these organisms as they are in vertebrates such as we describe a case study in which we use Biological Process gene sets derived from DFLAT-augmented GO annotation to analyze data from several previously published gene expression microarray experiments. Comparison of the analytical results to those derived from existing annotation demonstrates that Rabbit Polyclonal to RPS7 using the annotation and gene sets provided by DFLAT allows researchers to more accurately perform gene set.