De novo assembly and characterization of transcriptome towards understanding molecular mechanism associated with MYMIV-resistance in Vigna mungo - A computational study

Abstract

The fast climate change affects yield in Vigna mungo via enhancing both biotic and abiotic stresses.Out of all factors, the yellow mosaic disease has the most damaging effect. However, due to lack of reference genome of Vigna mungo, the complete mechanism associated with MYMIV (Mungbean Yellow Mosaic Indian Virus) resistance in Vigna mungo remain elusive to date. Considering this, the authors made an attempt to release new transcriptome and its annotation by employing computational approaches. Quality assessment of the generated transcriptomes reveals that it successfully aligned with 99.03% of the raw reads and hence can be employed for future research. Functional annotation of the transcriptome reveals that 31% and ~14% of the total transcripts encode lncRNAs and protein-coding sequences, respectively. Further, analysis reveals that, out of total transcripts, only 4536 and 78808 are significantly down and up-regulated during MYMIV infection in Vigna mungo, respectively. These significant transcripts are mainly associated with ribosome, spliceosome, glycolysis /gluconeogenesis, RNA transport, oxidative phosphorylation, protein processing in the endoplasmic reticulum, MAPK signaling pathway -plant, methionine and cysteine metabolism, purine metabolism and RNA degradation. Unlike the previous study, this is for the first time, the present study identified these pathways may play key role in MYMIV resistance in Vigna mungo. Thus, information and transcriptomes data available in the present study make a significant contribution to understanding the genomic structure of Vigna mungo, enabling future analyses as well as downstream applications of gene expression, sequence evolution, and genome annotation.

Next-generation development and application of codon model in evolution

“…The continuous growth of DNA and protein data has provided an opportunity to infer their function and their evolutionary history of any sequence/organisms in a more comprehensive way (Anisimova and Liberles, 2007;Miyazawa, 2011a;Dufresne and Jeffery, 2011;Gupta and Vadde, 2019a;Gupta et al, 2019;Gouda et al, 2020;Gupta et al, 2021a;Gupta et al, 2021b;Gupta et al, 2021c;Gupta et al, 2021d;Gupta et al, 2021e;Chu et al, 2021). Population genetics and phylogenetics are two of the most important subfields for inferring the evolutionary history of any sequences/organisms (Haubold, 2014).…”