Rapid construction of insect–plant interaction networks via multiplexed long‐fragment <scp>DNA</scp> metabarcoding and <scp>NGS</scp>

Abstract Molecular identification of insect food webs can accurately reveal complex trophic interactions and serve as a foundation for understanding ecosystem functioning and advancing biodiversity conservation. This process typically involves DNA library construction, high‐throughput sequencing and subsequent steps, including data assembly, annotation, denoising and taxonomic classification. However, the use of multiple long genetic markers substantially increases both the cost of high‐throughput sequencing and the complexity of downstream data analysis in insect diet studies. In this study, we developed a low‐cost, time‐efficient and highly accurate method for analysing the dietary habits of herbivorous insects and constructed an automated tool, ‘NGSdiet’, that processes high‐throughput sequencing data related to insect food webs to enable rapid identification of insect diets. We utilized four long‐fragment DNA barcodes (insect COI and plant rbcL , ITS and trnL ) in a multiplex PCR approach, which significantly reduced the cost of DNA library preparation. The resulting high‐throughput sequencing data can be automatically processed through a one‐click command using the NGSdiet tool, greatly simplifying the otherwise complex workflow. NGSdiet incorporates functions, such as BWA alignment, Trinity de novo assembly, sequencing depth calculation, adjacent‐base depth ratio (‘base_ratio’) filter, sequence annotation and barcode marker sorting. By setting thresholds for average barcode sequencing depth, length, base_ratio, and applying a sliding window based on base_ratio values, the tool effectively filters out errors caused by PCR chimeras, contamination or misassemblies. The workflow begins by aligning reads against a local reference database using BWA, followed by filtering and output of mapped results. If the initial alignment fails, the pipeline automatically initiates de novo assembly with Trinity and filtering. It also enables automatic separation of different genetic markers across samples. To validate the robustness of the method, three batches of Lepidoptera larval samples were subjected to sequencing analysis. The identification accuracies of the COI gene were 98.26%, 92.13% and 100%, respectively, while the combined plant barcodes achieved identification accuracies of 92.17%, 68.53% and 92.06%. Results demonstrate that NGSdiet is a fast, cost‐effective and highly sensitive tool for high‐throughput analysis. It also shows considerable potential for scalability, making it applicable to animal diet identification across various ecosystems.