Unveiling the possible floral visitors and invisible pollination networks from Deep RNA-seq Profile

Floral visitors are key components of successful plant sexual reproduction, although the lack of systematic monitoring, limits our understanding of the status of visitors to flowers. The classical methods of identification and quantification of floral visitors may require ecologists to reach and observe tall trees in dense forests and knowledge of entomology and taxonomy. The advancement of next-generation sequencing technologies has revolutionized traditional analytical methods in many fields. The pollen gets deposited on animals’ bodies, and the salivary glands, muscle cells, and secretions may retain on the flower parts. These insect body parts and secretions collected with flowers contain information about the insects that visit those flowers. By considering this, we present a workflow from freely available bioinformatics software for floral visitor detection based on the hypothesis that a small amount of Illumina Hi-seq RNA data is sufficient for detecting floral visitors. We assembled the workflow using deep Illumina Hi-seq data generated for Cinnamomum verum flowers and field observations. The workflow was also validated using five additional datasets from NCBI. Assembly was performed de novo and eliminated the redundancy. For the taxonomy analysis, assembled transcripts were blasted against insect taxid using DIAMOND BLAST. The same procedure was followed while randomly extracting 1-5 GB of raw data separately, examining the number of species and insect specific genes on the output. The floral visitors detected in transcriptome data of cinnamon were confirmed in the field. Interestingly, >95% of the floral visitors detected from the complete transcriptome data are represented in 1 GB of data. It needs about 0.1g of tissue to generate 1–5 GB of data for an affordable price. More importantly, this workflow could be utilized as the foundation for a detailed study of any plant species, which would help to streamline the identification and quantification of pollinators.