2017
DOI: 10.1101/237339
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Characterizing co-expression networks underpinning maize stalk rot virulence inFusarium verticillioidesthrough computational subnetwork module analyses

Abstract: 20 21Fusarium verticillioides is recognized as an important stalk rot pathogen of maize 22worldwide, but our knowledge of genetic mechanisms underpinning this pathosystem is limited. 23Previously, we identified a striatin-like protein Fsr1 that plays an important role in stalk rot. To 24 further characterize transcriptome networks downstream of Fsr1, we performed next-generation 25 sequencing (NGS) to investigate relative read abundance and also to infer co-expression 26 networks utilizing the preprocessed exp… Show more

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Cited by 3 publications
(4 citation statements)
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“…Beyond WGCNA, Saelens et al (2018) have systematically compared 42 different methods for clustering, decomposition, bi-clustering, and iterative network inference. These techniques have been applied in A. thaliana and other plants such as maize and wheat (Kim et al, 2018) to explore their interactions with microbes. The identified modules provide a first insight into genes sharing the same functionalities (Vella et al, 2017), and can help to achieve a better understanding of processes relevant for infection or commensalism.…”
Section: Transcriptional Regulatory Networkmentioning
confidence: 99%
“…Beyond WGCNA, Saelens et al (2018) have systematically compared 42 different methods for clustering, decomposition, bi-clustering, and iterative network inference. These techniques have been applied in A. thaliana and other plants such as maize and wheat (Kim et al, 2018) to explore their interactions with microbes. The identified modules provide a first insight into genes sharing the same functionalities (Vella et al, 2017), and can help to achieve a better understanding of processes relevant for infection or commensalism.…”
Section: Transcriptional Regulatory Networkmentioning
confidence: 99%
“…Furthermore, recent technological advancements in next 76 generation sequencing (NGS) and computational biology are facilitating new discoveries in gene 77 regulatory networks (Yoon & Qian, 2009;Sahraeian & Yoon, 2011). In our earlier studies (Kim 78 et al, 2018a;Kim et al, 2018b), we demonstrated how network-based comparative analysis of 79 transcriptome data through probabilistic subnetwork inference can help identify potential 80 pathogenicity-associated subnetwork modules in F. verticillioides. Plant-microbe associations 81 come in many different schemes, due to the intricate evolutionary relationship between the host 82 and the pathogen (Jones & Dangl, 2006;Spoel & Dong, 2012;Dangl et al, 2013).…”
Section: Introduction 47mentioning
confidence: 85%
“…We first preformed preprocessing the RNA-seq datasets by alignment using TopHat2 132 To search for genes encoding secreted protein, we assigned genes with signal peptide that were 138 7 significantly differentially expressed between the two maize kernels as seed genes by measuring 139 t-test statistics scores as well as F scores of ANOVA across all three PGEMs, thereby preparing 140 ten seed genes for each kernel (twenty in total). For co-expression network construction, we built 141 five different networks at five different levels (i.e., five different threshold cut-offs) as previously 142 applied (Kim et al, 2015;Kim et al, 2018a;Kim et al, 2018b), where the smallest size included 143 roughly 400,000 edges and the largest size contained around 2,000,000 edges. Additional detail 144 is provided in Supplementary Method A.…”
Section: Introduction 47mentioning
confidence: 99%
“…These experiments were carried out with three biological replicates. Stalk rot virulence assay was conducted using silver queen hybrid seeds as previously described with minor modifications (Kim et al ., ). Spores solutions (5 μl, 10 7 /mL) were collected from V8 plates.…”
Section: Methodsmentioning
confidence: 97%