MOTIVATION: Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce scRNAss, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. RESULTS: Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and Class2. In particular, it showed a remarkable capability of recovering unknown "novel" isoforms and highly computational efficiency compared to other tools. AVAILABILITY: scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. SUPPLEMENTARY INFORMATION: Supplementary data are Available at Bioinformatics Online.