PLANEX: the plant co-expression database

Yim, Won Cheol; Yu, Yongbin; Song, Kitae; Jang, Cheol Seong; Lee, Byung-Moo

doi:10.1186/1471-2229-13-83

Cited by 57 publications

(34 citation statements)

References 39 publications

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“…Several resources are already available for GCN analysis in maize, including COB (Schaefer et al, 2014), CORNET (De Bodt et al, 2012), CoP (Ogata et al, 2010), PLANEX (Yim et al, 2013), and ATTED-II ). All of the databases except ATTED-II used PCC to build GCN from 128 to 379 microarray datasets.…”

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confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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confidence: 99%

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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“…One of the perspectives resulting of the numerous available data is the analysis of expression profile per genes and the comparison between genes in order to produce expression gene cluster. Different web tools such as ATTED II (Aoki, et al, 2016), STRING database (Szklarczyk, et al, 2015), PlaNet (Ruprecht, et al, 2017), Planex (Yim, et al, 2013), CoP (Ogata et al, 2010) PlantExpress (Kudo, et al, 2017) ou COEXPRESSdb (Obayashi, et al, 2013) are available. The purpose of these programs is to represent expression gene cluster mainly for Arabidopsis thaliana.…”

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confidence: 99%

ExpressWeb: A Web application for clustering and visualization of expression data

Savelli

Picard

Roux

et al. 2019

Preprint

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The recent explosion of transcriptomics and proteomic data have resulted in vast amounts of datasets without connection and sometime too large to be easily analysed. Integration between datasets and analysis of an extracted datasets are limiting factors which need to be solved in order to make full use of the data and to connect data.ExpressWeb is an online web tool that combines a Taylor clustering of expressed data sets to extract gene network with gene annotations to visualise the co-expression network. Data sets can become from personal or publically experiments. ExpressWeb allows to easily compute clustering on expression data and provides friendly and useful visualisation tools as heatmaps, graphs and networks, generating output images which can be used for scientific publications.

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“…[26,27]). Among these, we here consider AtCOECiS [28], ATTED II [29], BAR [30], CoP [31], CORNET [32], Cress Express [33], CSB.DB [34], GeneCAT [35], GeneMania [36], Genevestigator [37], PlaNet [38].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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.

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PLANEX: the plant co-expression database

Cited by 57 publications

References 39 publications

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

ExpressWeb: A Web application for clustering and visualization of expression data

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

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