SQANTI-reads leverages SQANTI3, a tool for the analysis of the quality of transcript models, to develop a read-level quality control framework for replicated long-read RNA-seq experiments. The number and distribution of reads, as well as the number and distribution of unique junction chains (transcript splicing patterns), in SQANTI3 structural categories are informative of raw data quality. Multisample visualizations of QC metrics are presented by experimental design factors to identify outliers. We introduce new metrics for (1) the identification of potentially under-annotated genes and putative novel transcripts and for (2) quantifying variation in junction donors and acceptors. We applied SQANTI-reads to two different data sets, a