摘要：SummarySingle-cell RNA sequencing allows for characterizing the gene expression landscape at the cell type level. However, because of its use of short-reads, it is severely limited at detecting full-length features of transcripts such as alternative splicing. New library preparation techniques attempt to extend single-cell sequencing by utilizing both long-reads and short-reads. These techniques split the library material, after it is tagged with cellular barcodes, into two pools: one for short-read sequencing and one for long-read sequencing. However, the challenge of utilizing these techniques is that they require matching the cellular barcodes sequenced by the erroneous long-reads to the cellular barcodes detected by the short-reads. To overcome this challenge, we introduce scTagger, a computational method to match cellular barcodes data from long-reads and short-reads. We tested scTagger against another state-of-the-art tool on both real and simulated datasets, and we demonstrate that scTagger has both significantly better accuracy and time efficiency.Graphical abstractDisplay OmittedHighlights•We introduce scTagger, a method for matching long-read and short-read cellular barcodes•The matching enables combining gene expression analysis and isoform detection•scTagger relies on a trie-based data structure to perform this matching•We show that scTagger is accurate and fast on both real and simulated dataBioinformatics; Genomics; Sequence analysis
