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

Abstract

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...

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

“…Increasing sample size was reported to generally have a positive effect on network performance (Ballouz et al, 2015;Huang et al, 2017) -consistent with our comparison of tissue-specific or genotype-specific (i.e., developmental atlas) networks of different sizes. Interestingly, this is not always the case when comparing networks constructed from samples spanning multiple tissues of the same genotype panel (i.e., "metanetworks") to the various tissue-specific networks (i.e., using only samples from the same tissue for the genotype panel).…”

MaizeNet: a co‐functional network for network‐assisted systems genetics in Zea mays

“…This result can be attributed to either biased GOBP annotations toward evolutionarily conserved processes rather than plant-specific ones in maize, or biased availability of high-quality functional genomics data toward animals and yeast. Subsequently, we compared MaizeNet with other maize gene networks based on the protein-protein interaction database for maize (PPIM) (Zhu et al, 2016), gene coexpression (GCN) (Huang et al, 2017) and integration of multiple types of interactions (STRING10.5) (Szklarczyk et al, 2017) (Table 2). This comparison revealed that the range of AUROC (FPR < 0.01) for MaizeNet was significantly higher than that of the other maize gene networks (P < 0.001, Wilcoxon signed rank test) ( Figure 2b), thereby indicating that MaizeNet is among the most predictive gene networks for biological processes in maize.…”

Cytoscape stringApp: Network analysis and visualization of proteomics data

“…This is true for transcriptomics data, which can be imported and visualized on a network by following the same steps we showed for proteomics data, as well as for phenotypic screens and mutation data. For example, the stringApp has already been used in the literature for network analysis of microarray data on the mammalian circadian pacemaker 21 and for comparing a coexpression network obtained from maize RNA-seq data to a network from STRING 22 .…”