Lately, long read sequencing technologies, referred to as Third Generation Sequencing, (TGS, Pacific Bioscience [1] and Nanopore [2]) have brought the opportunity to sequence full-length RNA molecules. In doing so they relax the constraint of transcript reconstruction prior to study complete RNA transcripts. By avoiding limitations of previous technologies, [3, 4] and giving access to the trancripts structure, they might contribute to complement and improve transcriptomes studies. This is particularly crucial for non model species where assembly was required. Many biological questions (finding gene signatures for a trait, finding expressed variants...)[5, 6] are classically addressed using transcriptome sequencing. However, this gain in length is at the cost of a computationally challenging error rate (up to 15%) that disqualifies previous short-reads methods. In this work we propose to support the analysis of RNA long read sequencing with a clustering method that works at the gene level. It enables to group transcripts that emerged from a same gene. From the clusters, the expression of each gene is obtained and related transcripts are identied, even when no reference is available.