Decoding the effect of polyploidization on Morus species complex: an integrated transcriptomic and phenomic approach

Using biotechnological intervention to decode the genetic architecture of non-model crops, especially polyploid tree species, is challenging. The reconstruction of repository transcriptome analysis (RNA-Seq) is considered a leading-edge technique used to understand functional aspects of biological consequence; therefore, this technique is thought to be the only approach for deciphering genetic architecture. Currently, a large number of transcriptome datasets from various species are publicly available. Nevertheless, data processing and interpretation remain difficult due to a lack of critical coding skills, computing resources, and costly automated tools. Furthermore, in the non-model system, they became challenging due to inadequate replicates, the absence of the reference genome, and so on. To meet this demand, we introduced a state-of-the-art methodology for repository transcriptome dataset analysis, including downstream analysis like the taxonomic identity of the transcriptome or global transcriptome map/network and functional enrichment of overrepresented genes using publicly available web platforms and software. Here, mature leaf transcriptome data of wild Morus species belonging to different ploidy levels were sampled, and this revealed the strong biological effect of polyploidization. Further, to substantiate our prediction, we biologically validated the ploidy-associated attributes. Considering all this, the present research helps to understand the molecular basis of phenotype variation without prior coding knowledge and cost. The optimised protocol and generated data help to decode the biological consequences of the strong effect of polyploids on cellular as well as growth behavior levels for further conservation from the ongoing threat of climate change.