Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Li, Yupeng; Pearl, Stephanie A.; Jackson, Scott A.

doi:10.1016/j.tplants.2015.06.013

Cited by 75 publications

(65 citation statements)

References 101 publications

(129 reference statements)

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“…To optimize this step, the AUROC values of six correlation (PCC, SCC, KCC, GCC, BIC, and CSC) and four MI methods [additive ARACNE (AA), multiplicative ARACNE (MA), MRNET, and CLR) were compared for the expression matrices that were generated from each of three normalization methods (VST, CPM, and RPKM) and then averaged. In general, correlation methods are more computationally efficient whereas MI methods are able to reveal nonlinear relationships (Li et al, 2015c). PCC is widely used but may be influenced by outliers (Mukaka, 2012).…”

Section: Correlation Methods Perform Better Than MI At Some Genesmentioning

confidence: 99%

“…Although recent work has made substantial progress toward describing genomewide expression patterns in many genotypes, environmental conditions, and tissues, relatively little is known about the function and regulation of most maize genes. Because genes with related biological functions or regulatory mechanisms often have similar expression patterns (Aoki et al, 2007), one way to enhance understanding of gene function is by construction of a gene coexpression network (GCN;D'haeseleer et al, 2000;Aoki et al, 2007;Usadel et al, 2009;Li et al, 2015c;Serin et al, 2016). GCNs are constructed using data mining tools and algorithms that describe the relatedness between the expression patterns of multiple genes in a pairwise fashion.…”

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confidence: 99%

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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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Section: Correlation Methods Perform Better Than MI At Some Genesmentioning

confidence: 99%

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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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“…The outcome is the identification of groups of genes (modules) with similar expression behaviour, suggesting that they are under common transcriptional control. Modules (represented by module eigengenes) can be correlated with phenotypic variation to identify modules that may influence the trait (Li et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Systems genetics reveals a transcriptional network associated with susceptibility in the maize–grey leaf spot pathosystem

et al. 2017

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We used a systems genetics approach to elucidate the molecular mechanisms of the responses of maize to grey leaf spot (GLS) disease caused by Cercospora zeina, a threat to maize production globally. Expression analysis of earleaf samples in a subtropical maize recombinant inbred line population (CML444 × SC Malawi) subjected in the field to C. zeina infection allowed detection of 20 206 expression quantitative trait loci (eQTLs). Four trans-eQTL hotspots coincided with GLS disease QTLs mapped in the same field experiment. Co-expression network analysis identified three expression modules correlated with GLS disease scores. The module (GY-s) most highly correlated with susceptibility (r = 0.71; 179 genes) was enriched for the glyoxylate pathway, lipid metabolism, diterpenoid biosynthesis and responses to pathogen molecules such as chitin. The GY-s module was enriched for genes with trans-eQTLs in hotspots on chromosomes 9 and 10, which also coincided with phenotypic QTLs for susceptibility to GLS. This transcriptional network has significant overlap with the GLS susceptibility response of maize line B73, and may reflect pathogen manipulation for nutrient acquisition and/or unsuccessful defence responses, such as kauralexin production by the diterpenoid biosynthesis pathway. The co-expression module that correlated best with resistance (TQ-r; 1498 genes) was enriched for genes with trans-eQTLs in hotspots coinciding with GLS resistance QTLs on chromosome 9. Jasmonate responses were implicated in resistance to GLS through co-expression of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex' in the TQ-r module. Consistent with this, JAZ repressor expression was highly correlated with the severity of GLS disease in the GY-s susceptibility network.

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“…Network-based gene prioritization approaches has been widely applied to identify new genes involved in biological processes of interests (Li et al, 2015), such as abiotic stress responses (Ma et al, 2014; Sircar and Parekh, 2015), secondary wall formation (Ruprecht et al, 2011), glucosinolate secondary metabolism (Chan et al, 2011), and plant growth (Sabaghian et al, 2015). In this study, we presented an integrative random forest method called RafSee and a meta-analysis based approach called RAP to prioritize genes from a large set of candidates.…”

Section: Discussionmentioning

confidence: 99%

“…A major challenge in plant biology is to identify the most promising genes from large lists of candidate genes (e.g., all genes in the whole genome) to find those which play an important role in an agricultural trait or a complex biological process (Lee et al, 2010; Li et al, 2015; Sabaghian et al, 2015). However, an experimental validation of every candidate gene is very time-consuming and costly.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

et al. 2016

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The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus, a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Toward this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The “leave-one-out” cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.

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Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Cited by 75 publications

References 101 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

Systems genetics reveals a transcriptional network associated with susceptibility in the maize–grey leaf spot pathosystem

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

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