Single-cell long-read sequencing of transcriptomes is a novel sequencing technique where both experimental protocols and computational bioinformatic protocols are being established. In this larger project, we aim to design novel computational methods for the analysis of such data. The project concerns improving both the accuracy in isoform prediction, demultiplexing of noisy reads into cells, as well as clustering and consensus forming of reads from the same transcript. Resources will be used for developing and evaluating our methods and pipelines to produce faster and more accurate analyses of such data. Our ability to use the computational resources from this project has resulted in several publications in high-impact journals [1-4]. In collaboration with NBIS we also maintain and further develop software we published [3]. We are five people working on this project (my three PhD students, Marcel Martin from NBIS, and I). [1] Kristoffer Sahlin, Veli Mäkinen, Accurate spliced alignment of long RNA sequencing reads, Bioinformatics, 2021, btab540, https://doi.org/10.1093/bioinformatics/btab540 [2] Kristoffer Sahlin, Effective sequence similarity search with strobemers. Genome Research, 01/11/2021 doi: https://doi.org/10.1101/gr.275648.121 [3] Kristoffer Sahlin, Strobealign: flexible seed size enables ultra-fast and accurate read alignment. Genome Biol 23, 260 (2022). https://doi.org/10.1186/s13059-022-02831-7 [4] Alexander J Petri, Kristoffer Sahlin, isONform: reference-free transcriptome reconstruction from Oxford Nanopore data, Bioinformatics, Volume 39, Issue Supplement_1, June 2023, Pages i222–i231, https://doi.org/10.1093/bioinformatics/btad264