Transcriptome Assembly at Single-Cell Resolution with Beaver

Abstract

Emerging single-cell RNA sequencing techniques (scRNA-seq) has enabled the study of cellular transcriptome heterogeneity, yet accurate reconstruction of full-length transcripts at single-cell resolution remains challenging due to high dropout rates and sparse coverage. While meta-assembly approaches offer promising solutions by integrating information across multiple cells, current methods struggle to balance consensus assembly with cell-specific transcriptional signatures. Here, we present Beaver, a cell-specific transcript assembler designed for short-read scRNA-seq data. Beaver implements a transcript fragment graph to organize individual assemblies and designs an efficient dynamic programming algorithm that searches for candidate full-length transcripts from the graph. Beaver in-corporates two random forest models trained on 51 meticulously engineered features that accurately estimate the likelihood of each candidate transcript being expressed in individual cells. Our experiments, performed using both real and simulated Smart-seq3 scRNA-seq data, firmly show that Beaver substantially outperforms existing meta-assemblers and single-sample assemblers. At the same level of sensitivity, Beaver achieved 32.0%-64.6%, 13.5%-36.6%, and 9.8%-36.3% higher precision in average compared to meta-assemblers Aletsch, TransMeta, and PsiCLASS, respectively, with similar improvements over single-sample assemblers Scallop2 (10.1%-43.6%) and StringTie2 (24.3%-67.0%). Beaver is freely available at https://github.com/Shao-Group/beaver. Scripts that reproduce the experimental results of this manuscript are available at https://github.com/Shao-Group/beaver-test.