<p>Long-read sequencing (LRS) platforms, such as Oxford Nanopore and Pacific Biosciences, enable comprehensive transcriptome analysis but face challenges such as sequencing errors, sample quality variability, and library preparation biases. Current benchmarking approaches address these issues insufficiently: BUSCO assesses transcriptome completeness using conserved single-copy orthologous genes but can misinterpret alternative splicing as gene duplications, while SIRV spike-ins and ERCCs oversimplify real sample complexity, neglecting RNA degradation and RNA-extraction artifacts, thus inflating performance metrics. Simulation algorithms are limited in their ability to recapitulate the complexity of real samples. To overcome these limitations, we introduce the Transcriptome Universal Single-isoform COntrol (TUSCO) benchmarking framework, centered on a curated TUSCO gene set of genes lacking alternative isoforms that can be confidently treated as an internal ground truth. The TUSCO evaluation quantifies precision by identifying reconstructed transcripts that deviate from reference annotations and quantifies sensitivity by verifying detection completeness in human and mouse samples. Masking TUSCO gene set transcripts and replacing them with modified splice variants in the annotation creates a TUSCO-novel challenge that assesses reconstruction of the true, now-unannotated isoforms. Our validation demonstrates that TUSCO metrics provide accurate and reliable benchmarking without external controls, significantly improving quality control standards for transcriptome reconstruction using LRS.</p>