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G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes.

ABSTRACT: In previous studies, gene neighborhoods-spatial clusters of co-expressed genes in the genome-have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST) which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes simultaneously.Using G-NEST on atlases of mouse and human tissue expression data, we found that large neighborhoods of ten or more genes are extremely rare in mammalian genomes. When they do occur, neighborhoods are typically composed of families of related genes. Both the highest scoring and the largest neighborhoods in mammalian genomes are formed by tandem gene duplication. Mammalian gene neighborhoods contain highly and variably expressed genes. Co-localized noisy gene pairs exhibit lower evolutionary conservation of their adjacent genome locations, suggesting that their shared transcriptional background may be disadvantageous. Genes that are essential to mammalian survival and reproduction are less likely to occur in neighborhoods, although neighborhoods are enriched with genes that function in mitosis. We also found that gene orientation and protein-protein interactions are partially responsible for maintenance of gene neighborhoods.Our experiments using G-NEST confirm that tandem gene duplication is the primary driver of non-random gene order in mammalian genomes. Non-essentiality, co-functionality, gene orientation, and protein-protein interactions are additional forces that maintain gene neighborhoods, especially those formed by tandem duplicates. We expect G-NEST to be useful for other applications such as the identification of core regulatory modules, common transcriptional backgrounds, and chromatin domains. The software is available at http://docpollard.org/software.html.

SUBMITTER: Lemay DG

PROVIDER: S-EPMC3575404 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes.

Lemay Danielle G DG Martin William F WF Hinrichs Angie S AS Rijnkels Monique M German J Bruce JB Korf Ian I Pollard Katherine S KS

BMC bioinformatics 20120928

<h4>Background</h4>In previous studies, gene neighborhoods-spatial clusters of co-expressed genes in the genome-have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST) which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes ...[more]

PMID: 23020263

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Project description:BackgroundComparative genomic analysis using cDNA microarray is a new approach and a useful tool to identify important genetic sequences or genes that are conserved throughout evolution. Identification of these conserved sequences will help elucidate important molecular mechanisms or pathways common to many species. For example, the stockpiled transcripts in the oocyte necessary for successful fertilization and early embryonic development still remain relatively unknown. The objective of this study was to identify genes expressed in oocytes and conserved in three evolutionarily distant species.ResultsIn this study we report the construction of a multi-species cDNA microarray containing 3,456 transcripts from three distinct oocyte-libraries from bovine, mouse and Xenopus laevis. Following the cross-species hybridizations, data analysis revealed that 1,541 positive hybridization signals were generated by oocytes of all three species, and 268 of these are preferentially expressed in the oocyte. Data reproducibility analyses comparing same-species to cross-species hybridization indicates that cross-species hybridizations are highly reproducible, thus increasing the confidence level in their specificity. A validation by RT-PCR using gene- and species-specific primers confirmed that cross-species hybridization allows the production of specific and reliable data. Finally, a second validation step through gene-specific microarray hybridizations further supported the validity of our cross-species microarray results. Results from these cross-species hybridizations on our multi-species cDNA microarray revealed that SMFN (Small fragment nuclease), Spin (Spindlin), and PRMT1 (Protein arginine methyltransferase 1) are transcripts present in oocytes and conserved in three evolutionarily distant species.ConclusionCross-species hybridization using a multi-species cDNA microarray is a powerful tool for the discovery of genes involved in evolutionarily conserved molecular mechanisms. The present study identified conserved genes in the oocytes of three distant species that will help understand the unique role of maternal transcripts in early embryonic development.

Project description:Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease.We have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency >1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome.Our results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us.

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G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes.

Publications

G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes.

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