ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

Obayashi, Takeshi; Kinoshita, Kengo; Nakai, Kenta; Shibaoka, Masayuki; Hayashi, Shinji; Saeki, Motoshi; Shibata, Daisuke; Saito, Kazuki; Ohta, Hiroyuki

doi:10.1093/nar/gkl783

Cited by 340 publications

(303 citation statements)

References 43 publications

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“…More specifically, a gene co-expression database comprising 19,777 human genes was downloaded from COXPRESSdb [45]. To measure the similarity between a pair of genes, we employed the mutual rank method, which evaluates the strength of coexpression [46]. The mutual rank ranges from 0 to 19,776 and the normalized value w i j = (19,776…”

Section: Gene/protein Network Protein-protein Interaction Networkmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

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Section: Gene/protein Network Protein-protein Interaction Networkmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

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“…Twenty-four genes (listed in Table 2) were used as core query genes. The genes exhibiting positive correlations (r > 0.525) against the flavonoid biosynthetic genes were extracted using ATTED-II (Obayashi et al, 2007) based on all data sets version 3 in AtGenExpress. The coexpression graph was depicted by the Pajek program.…”

Section: Transcriptome Coexpression Analysismentioning

confidence: 99%

Comprehensive Flavonol Profiling and Transcriptome Coexpression Analysis Leading to Decoding Gene–Metabolite Correlations inArabidopsis

et al. 2008

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To complete the metabolic map for an entire class of compounds, it is essential to identify gene-metabolite correlations of a metabolic pathway. We used liquid chromatography-mass spectrometry (LC-MS) to identify the flavonoids produced by Arabidopsis thaliana wild-type and flavonoid biosynthetic mutant lines. The structures of 15 newly identified and eight known flavonols were deduced by LC-MS profiling of these mutants. Candidate genes presumably involved in the flavonoid pathway were delimited by transcriptome coexpression network analysis using public databases, leading to the detailed analysis of two flavonoid pathway genes, UGT78D3 (At5g17030) and RHM1 (At1g78570). The levels of flavonol 3-Oarabinosides were reduced in ugt78d3 knockdown mutants, suggesting that UGT78D3 is a flavonol arabinosyltransferase. Recombinant UGT78D3 protein could convert quercetin to quercetin 3-O-arabinoside. The strict substrate specificity of UGT78D3 for flavonol aglycones and UDP-arabinose indicate that UGT78D3 is a flavonol arabinosyltransferase. A comparison of flavonol profile in RHM knockout mutants indicated that RHM1 plays a major role in supplying UDPrhamnose for flavonol modification. The rate of flavonol 3-O-glycosylation is more affected than those of 7-O-glycosylation by the supply of UDP-rhamnose. The precise identification of flavonoids in conjunction with transcriptomics thus led to the identification of a gene function and a more complete understanding of a plant metabolic network.

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“…The amino acid sequence of OST3/6 displays 65% identity (80% similarity) to OST3/6-LIKE1, and the overall protein topology is conserved between OST3/6 and OST3/6-LIKE1. Extraction of data from microarray experiments (Obayashi et al, 2007) reveals that OST3/6 is widely expressed in Arabidopsis, and OST3/6 transcript expression correlates well with the expression of other genes coding for OST subunits like STT3A and DAD1 (Supplemental Fig. S1; Supplemental Table S3).…”

Section: Arabidopsis Contains Two Ost3/6 Homologsmentioning

confidence: 99%

Specialized Roles of the Conserved Subunit OST3/6 of the Oligosaccharyltransferase Complex in Innate Immunity and Tolerance to Abiotic Stresses

et al. 2013

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Asparagine-linked glycosylation of proteins is an essential cotranslational and posttranslational protein modification in plants. The central step in this process is the transfer of a preassembled oligosaccharide to nascent proteins in the endoplasmic reticulum by the oligosaccharyltransferase (OST) complex. Despite the importance of the catalyzed reaction, the composition and the function of individual OST subunits are still ill defined in plants. Here, we report the function of the highly conserved OST subunit OST3/6. We have identified a mutant in the OST3/6 gene that causes overall underglycosylation of proteins and affects the biogenesis of the receptor kinase EF-TU RECEPTOR involved in innate immunity and the endo-b-1,4-glucanase KORRIGAN1 required for cellulose biosynthesis. Notably, the ost3/6 mutation does not affect mutant variants of the receptor kinase BRASSINOSTEROID-INSENSITIVE1. OST3/6 deficiency results in activation of the unfolded protein response and causes hypersensitivity to salt/osmotic stress and to the glycosylation inhibitor tunicamycin. Consistent with its role in protein glycosylation, OST3/6 resides in the endoplasmic reticulum and interacts with other subunits of the OST complex. Together, our findings reveal the importance of Arabidopsis (Arabidopsis thaliana) OST3/6 for the efficient glycosylation of specific glycoproteins involved in different physiological processes and shed light on the composition and function of the plant OST complex.

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ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis

Cited by 340 publications

References 43 publications

Ontology-based disease similarity network for disease gene prediction

Ontology-based disease similarity network for disease gene prediction

Comprehensive Flavonol Profiling and Transcriptome Coexpression Analysis Leading to Decoding Gene–Metabolite Correlations inArabidopsis

Specialized Roles of the Conserved Subunit OST3/6 of the Oligosaccharyltransferase Complex in Innate Immunity and Tolerance to Abiotic Stresses

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