soyfungigcn

SoyFungiGCN is a web application and R/Shiny package that integrates publicly available genome-wide association studies (GWAS) and transcriptomic data to prioritize candidate genes associated with resistance to five major fungal diseases in soybean: Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. The tool identifies high-confidence candidate genes for each fungal pathogen. It has a user-friendly interface to explore the coexpression network of soybean-pathogenic fungi interactions at the transcriptional level. SoyFungiGCN also helps identify the most resistant soybean accessions against each fungal species based on the number of resistance alleles, which can be used for further improvement in breeding programs or through genetic engineering.

Topic

Model organisms;GWAS study;Plant biology;Gene expression;RNA-Seq

Detail

  • Operation: Network visualisation;Homology-based gene prediction

  • Software interface: Library

  • Language: R

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil, Conselho Nacional de Desenvolvimento Científico e Tecnológico.

  • Input: -

  • Output: -

  • Contact: Thiago M. Venancio thiago.venancio@gmail.com ,Fabricio Almeida-Silva fabricio_almeidasilva@hotmail.com

  • Collection: -

  • Maturity: -

Publications

  • Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens.
  • Almeida-Silva F and Venancio TM. Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens. Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens. 2021; 11:24453. doi: 10.1038/s41598-021-03864-x
  • https://doi.org/10.1038/s41598-021-03864-x
  • PMID: 34961779
  • PMC: PMC8712514

Download and documentation

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