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Simplified ontologies allowing comparison of developmental mammalian gene expression.

ABSTRACT: Model organisms represent an important resource for understanding the fundamental aspects of mammalian biology. Mapping of biological phenomena between model organisms is complex and if it is to be meaningful, a simplified representation can be a powerful means for comparison. The Developmental eVOC ontologies presented here are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy. We demonstrate the ontologies by identifying genes showing a bias for developmental brain expression in human and mouse.

SUBMITTER: Kruger A

PROVIDER: S-EPMC2246303 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Simplified ontologies allowing comparison of developmental mammalian gene expression.

Kruger Adele A Hofmann Oliver O Carninci Piero P Hayashizaki Yoshihide Y Hide Winston W

Genome biology 20070101 10

Model organisms represent an important resource for understanding the fundamental aspects of mammalian biology. Mapping of biological phenomena between model organisms is complex and if it is to be meaningful, a simplified representation can be a powerful means for comparison. The Developmental eVOC ontologies presented here are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy. We demonstrate the ontologies by identifying ...[more]

PMID: 17961239

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Project description:Sensitivity and throughput of transcriptomics and proteomomics technologies have advanced tremendously in recent years. With the use of deep sequencing of RNA samples (RNAseq) and mass spectrometry technology for protein identification and quantitation, it is now feasible to compare gene and protein expression on a massive scale and on any organism for which genomic data is available. Although these technologies are currently applied to many research questions in various model systems ranging from cell cultures to the entire organism level, there are few comparative studies of these technologies in the same system, let alone on the same samples. Here we present a comparison between gene and protein expression in zebrafish embryos, which is an upcoming model in disease studies. We compared Agilent custom made expression microarrays with Illumina deep sequencing for RNA analysis, showing as expected a high degree of correlation of expression of a common set of 15,927 genes. Gene expression was also found to correlate with the abundance of 1,017 quantified proteins, with several categories of genes as exceptions. These exceptions include ribosomal proteins, histones and vitellogenins, for which biological and technical explanations are discussed. By comparing state of the art transcriptomic and proteomic technologies on samples derived from the same group of organisms we have for the first time benchmarked the differences in these technologies with regard to sensitivity and bias towards detection of particular gene categories. Our datasets submitted to public repositories are a good starting point for researchers interested in disease progression in zebrafish at a stage of development highly suited for high throughput screening technologies. The transcriptomics data is deposited in NCBI GEO. Proteomics informatics: Spectra were identified by Mascot and validated by PeptideProphet and ProteinProphet in the Trans-Proteomic Pipeline (TPP). The main search parameters are: full tryptic specificity, precursor mass searched within -0.5 to +2.5 Da, product ions within [-0.5,0.5] Da and allowing for one missed cleavage. Carbamidomethylation was assumed as the only and fixed PTM.

Dataset Information

Simplified ontologies allowing comparison of developmental mammalian gene expression.

Publications

Simplified ontologies allowing comparison of developmental mammalian gene expression.

Similar Datasets

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