Transcriptomics provides a link between the genome, proteome, and cellular phenotype; therefore, it is regarded as a unique approach to revealing the molecular basis of phenotypes. Despite the availability of huge datasets for a wide range of taxa in public databases, RNA-Seq data analysis and interpretation remain challenging. This is due to factors including limited processing capability and the need for specialized coding expertise and expensive software. Here, we used publicly available RNA-Seq datasets and emphasized an in-depth description of each cutting-edge system/tool, especially Galaxy platform and TRAPID 2. We also conducted a downstream analysis ofthe authenticity of the taxonomic identity, the global transcriptome map/network, and the functional enrichment of overrepresented genes in silkworm (Bombyx mori L.) and mulberry (Morus spp.). We provided a flexible de novo assembly, annotations, and sufficient visualization to interpret the RNA-Seq analysis data. The current data-mining approach sheds light on the significant influence of silkworm black dilute (bd) mutants on melanogenesis and tyrosine degradation, which is mediated by dopachrome tautomerase (DTC). However, M. serrata revealed significant polyploid trade-offs, such as investments in biochemical expenditure, nucleic acid metabolism, the intrinsic activity of transposable elements, and a functional association with chloroplasts. Overall, the current data-mining method reduces costs and requires no prior coding expertise to comprehend the genetic foundation of phenotype variance. The present approach can also help to decode the genetic architecture of novel phenotypes, including trait-specific accessions, ploidy-associated traits, and the hybridity effect of not only Seri-genetic resources but also other model and non-model organisms.