Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Obayashi, Takeshi; Kinoshita, Kengo

doi:10.1093/dnares/dsp016

Cited by 216 publications

(240 citation statements)

References 40 publications

(58 reference statements)

Supporting

Mentioning

237

Contrasting

Order By: Relevance

“…ATTED-II (Obayashi and Kinoshita, 2009) and visualized the highly correlated genes with the JAM-regulated genes (MR , 50) by Cytoscape (Supplemental Fig. S7; see "Materials and Methods"; Smoot et al, 2011).…”

Section: Correlated Expression Between Jam-regulated Transcription Famentioning

confidence: 99%

See 1 more Smart Citation

Basic Helix-Loop-Helix Transcription Factors JASMONATE-ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3 Are Negative Regulators of Jasmonate Responses in Arabidopsis

et al. 2013

View full text Add to dashboard Cite

Jasmonates regulate transcriptional reprogramming during growth, development, and defense responses. Jasmonoyl-isoleucine, an amino acid conjugate of jasmonic acid (JA), is perceived by the protein complex composed of the F-box protein CORONATINE INSENSITIVE1 (COI1) and JASMONATE ZIM DOMAIN (JAZ) proteins, leading to the ubiquitin-dependent degradation of JAZ proteins. This activates basic helix-loop-helix-type MYC transcription factors to regulate JA-responsive genes. Here, we show that the expression of genes encoding other basic helix-loop-helix transcription factors, JASMONATE ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3, is positively regulated in a COI1-and MYC2-dependent manner in Arabidopsis (Arabidopsis thaliana). However, contrary to myc2, the jam1jam2jam3 triple mutant exhibited shorter roots when treated with methyl jasmonate (MJ), indicating enhanced responsiveness to JA. Our genome-wide expression analyses revealed that key jasmonate metabolic genes as well as a set of genes encoding transcription factors that regulate the JA-responsive metabolic genes are negatively regulated by JAMs after MJ treatment. Consistently, loss of JAM genes resulted in higher accumulation of anthocyanin in MJ-treated plants as well as higher accumulation of JA and 12-hydroxyjasmonic acid in wounded plants. These results show that JAMs negatively regulate the JA responses in a manner that is mostly antagonistic to MYC2.

show abstract

Section: Correlated Expression Between Jam-regulated Transcription Famentioning

confidence: 99%

“…For gene network analysis, we calculated the MR (Obayashi and Kinoshita, 2009) of the 98 JAM-regulated genes using ATTED-II and visualized the gene network based on the MR values by Cytoscape (http://www.cytoscape.org/; Smoot et al, 2011). We selected correlations whose MR values were less than 50.…”

Section: Gene Network Analysismentioning

confidence: 99%

Basic Helix-Loop-Helix Transcription Factors JASMONATE-ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3 Are Negative Regulators of Jasmonate Responses in Arabidopsis

et al. 2013

View full text Add to dashboard Cite

show abstract

“…This approach expands the view from lists of genes to graphs of genes, where connections are based on co-expression. This can be accomplished considering simple ranks and/or significance of pair-wise correlation values [59], but also exploiting more sophisticated algorithms [60]. Some of the platforms we have reviewed here provide embedded network generation tools (Additional Methods in the Supplementary Material [61]) or data formats suitable for network visualization by appropriate tools, among which Cytoscape is one of the most commonly used [62].…”

Section: Comparative Meta-analysis Of Results From Different Platformsmentioning

confidence: 99%

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

et al. 2016

View full text Add to dashboard Cite

Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

show abstract

“…In the literature, gene co-expression networks are also known as gene relevance, gene association or gene interaction networks. Different versions of the standard correlation-based method exist, such as one by Obayashi and Kinoshita in [19] that instead of correlation values uses correlation ranks.…”

Section: Inferring Gene Network: a Reviewmentioning

confidence: 99%

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Martínez-Ballesteros

Nepomuceno-Chamorro

Riquelme

2014

Journal of Computer and System Sciences

View full text Add to dashboard Cite

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. 1.IntroductionSince late 1990s, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. Microarray technology has revolutionized the biological research because it allows to study thousand of genes or even whole genomes [1].As molecular biology is rapidly evolving into a quantitative science, it increasingly relies on computational algorithms to make sense of high-throughput data. One of the main goals in Microarray analysis is the reconstruction of gene regulatory processes and a key task is the inference of regulatory interactions among genes from gene expression data [2].Our aimisto infer the relationships between genes from an organism in a particular biological process. This relationships can be modeled in several levels of abstraction, these levels range from the detailed gene regulatory processes (where a chain of intracellular reaction activates a regulatory molecule, transcription factors until a protein is synthesized) to the high models of abstraction named gene association networks. In the reconstruction of gene regulatory processes, building gene association networks has been proven to provide useful insights for such task, the reconstruction of gene regulatory processes. A gene association network can be defined as a graph in which nodes represent genes and edges represent the influence between them. Our goal in this work is the inference of gene association networks from Microarray datasets.There are several statistical methods to infer gene association networks from Microarray data. A microarray dataset is a bidimensional data structure where conditions are experiments or sources and the columns are gene expression values. In our problem the conditions will be the instances and the gene expression values will be the attributes or features. These methods range from relatively straightforward correlation-based methods to more sophisticated methods based on

show abstract

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Cited by 216 publications

References 40 publications

Basic Helix-Loop-Helix Transcription Factors JASMONATE-ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3 Are Negative Regulators of Jasmonate Responses in Arabidopsis

Basic Helix-Loop-Helix Transcription Factors JASMONATE-ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3 Are Negative Regulators of Jasmonate Responses in Arabidopsis

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

Discovering gene association networks by multi-objective evolutionary quantitative association rules

Contact Info

Product

Resources

About