MaizeNet: a co‐functional network for network‐assisted systems genetics in <i>Zea mays</i>

Lee, Tak H.; Lee, Sungho; Yang, Sunmo; Lee, Insuk

doi:10.1111/tpj.14341

Cited by 22 publications

(26 citation statements)

References 61 publications

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“…observed contribution of yeast-derived information to BarleyNet was expected. In addition, we previously observed a large contribution of animal-derived information during the construction of co-functional gene networks for other plant species (Lee et al, 2010;Lee et al, 2019). Thus, we confirmed the usefulness of information derived from non-plant species in the reconstruction of a co-functional network of plant genes.…”

Section: Construction Of Barleynet Via the Integration Of Omics Data supporting

confidence: 82%

“…For protein homology mapping between barley and other species, we used InParanoid (Remm et al, 2001), which provides sensitive orthology mapping by taking account of co-orthologs. Associalogs were then transferred from a total of 21 co-functional networks for nine other species: AraNet v2 (Lee et al, 2015b), MaizeNet (Lee et al, 2019), RiceNet v2 (Lee et al, 2015a), HumanNet v2 (Hwang et al, 2018), MouseNet v2 , DanioNet , WormNet v3 (Cho et al, 2014), FlyNet , and YeastNet v3 .…”

Section: Inferring Co-functional Links By Transferring Orthologous Gementioning

confidence: 99%

“…Co-functional gene networks have been applied to integrate the functional information of genes derived from heterogeneous data through a Bayesian statistics framework . Co-functional networks previously constructed for other major crop species have been successfully used in the genetic dissection of complex plant traits (Lee et al, 2015a;Lee et al, 2017;Lee et al, 2019). Yet, such an effective network-assisted systems genetics platform has not been developed for barley.…”

Section: Introductionmentioning

confidence: 99%

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BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.

Lee

Yang

et al. 2020

Front. Plant Sci.

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Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http:// www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.

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Section: Construction Of Barleynet Via the Integration Of Omics Data supporting

confidence: 82%

Section: Inferring Co-functional Links By Transferring Orthologous Gementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.

Lee

Yang

et al. 2020

Front. Plant Sci.

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“…The putative GbCGs were used to discover other functionally connected genes located within the multiple insect resistance genomic regions (MIRGRs) determined in a previous study [ 16 ]. The maize co-functional network database, MaizeNet ( ) [ 78 ] was used to identify maize genes functionally connected to the GbCGs used as guide genes. The network-based CGs (NbCGs) with connectivity scores to the guide genes higher than 5 were assessed for in silico differential expression using the Gene Expression Atlas (GXE) [ 75 ] with the same parameters as described earlier in this paper.…”

Section: Pre-cgs Prioritization Through a Suite Of Functional Charmentioning

confidence: 99%

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

Badji

Kwemoi

Machida³

et al. 2020

Genes

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Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

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“…Panzea (https://www.panzea.org/) collects genotypic and phenotypic information for several maize populations (19). while MaizeNet (http://www.inetbio.org/maizenet/) provides a genome-scale cofunctional network of maize genes (20). Other generic databases such as Genbank (https://www.ncbi.nlm.nih.gov/genbank/), Gramene (http://www.gramene.org/) and ePlant (http://bar.utoronto.ca/eplant_maize/) also collect maize omics data.…”

Section: Introductionmentioning

confidence: 99%

ZEAMAP, a comprehensive database adapted to the maize multi-omics era

Gui

Yang

et al. 2020

Preprint

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As one of the most extensively cultivated crops, maize (Zea mays L.) has been extensively studied by researchers and breeders for over a century. With advances in high-throughput detection of various omics data, a wealth of multi-dimensional and multi-omics information has been accumulated for maize and its wild relative, teosinte. Integration of this information has the potential to accelerate genetic research and generate improvements in maize agronomic traits. To this end, we constructed ZEAMAP (http://www.zeamap.com), a comprehensive database incorporating multiple reference genomes, annotations, comparative genomics, transcriptomes, open chromatin regions, chromatin interactions, high-quality genetic variants, phenotypes, metabolomics, genetic maps, genetic mapping loci, population structures and domestication selection signals between teosinte and maize. ZEAMAP is user-friendly, with the ability to interactively integrate, visualize and cross-reference multiple different omics datasets. and linkage analysis, the population structure and pedigrees of each germplasm and the selective signals between different teosinte subspecies and maize. ZEAMAP generated comprehensive functional annotations for the annotated gene models in each assembly, and provided useful tools for users to search, analyze and visualize all these different omics data.

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

Cited by 22 publications

References 61 publications

BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.

BarleyNet: A Network-Based Functional Omics Analysis Server for Cultivated Barley, Hordeum vulgare L.

Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization

ZEAMAP, a comprehensive database adapted to the maize multi-omics era

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