MrNV

MrNV is a software tool to analyze the transcriptome of the giant river prawn, Macrobrachium rosenbergii, mainly focusing on the post-larvae stage and their immune response to the Macrobrachium rosenbergii nodavirus (MrNV) infection, which causes White Tail Disease (WTD). The tool utilizes high-quality transcriptome data with a completeness of 83.4% and identifies differentially abundant transcripts during MrNV infection. MrNV validates these transcripts using qPCR and highlights key players in the antiviral immune response, such as antiviral proteins, C-type lectins, prophenol oxidase, caspase, ADP ribosylation factors, and dicer, which exhibit the most significant expression changes in the post-larvae after MrNV infection.

Topic

RNA-Seq;Transcriptomics;Sequence assembly;Gene transcripts;Molecular interactions, pathways and networks

Detail

  • Operation: Sequence trimming;De-novo assembly;RNA-Seq quantification;Transcriptome assembly

  • Software interface: Command-line interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: Strategic Wisdom and Research Institute, Srinakharinwirot University, Science Achievement Scholarship of Thailand, and Natural Sciences and Engineering Research Council of Canada.

  • Input: -

  • Output: -

  • Contact: Parin Chaivisuthangkura parin@g.swu.ac.th

  • Collection: -

  • Maturity: -

Publications

  • Transcriptomic analysis of Macrobrachium rosenbergii (giant fresh water prawn) post-larvae in response to M. rosenbergii nodavirus (MrNV) infection: de novo assembly and functional annotation.
  • Pasookhush P, et al. Transcriptomic analysis of Macrobrachium rosenbergii (giant fresh water prawn) post-larvae in response to M. rosenbergii nodavirus (MrNV) infection: de novo assembly and functional annotation. Transcriptomic analysis of Macrobrachium rosenbergii (giant fresh water prawn) post-larvae in response to M. rosenbergii nodavirus (MrNV) infection: de novo assembly and functional annotation. 2019; 20:762. doi: 10.1186/s12864-019-6102-6
  • https://doi.org/10.1186/S12864-019-6102-6
  • PMID: 31640560
  • PMC: PMC6805343

Download and documentation

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