Transcriptome assembly at single-cell resolution with Beaver.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables gene expression measurement at single-cell resolution.
• Reconstructing full-length transcripts from scRNA-seq data remains a significant challenge.
• Existing assembly methods struggle to balance consensus and cell-specific expression.

Purpose of the Study:

• To develop a novel cell-specific transcript assembler for short-read scRNA-seq data.
• To improve the accuracy and efficiency of full-length transcript reconstruction.
• To overcome limitations of current single-sample and meta-assembly approaches.

Main Methods:

• Developed Beaver, a cell-specific transcript assembler utilizing a transcript fragment graph.
• Implemented an efficient dynamic programming algorithm for candidate transcript identification.
• Integrated two random forest models with 51 engineered features to predict transcript expression likelihood.

Main Results:

• Beaver demonstrated substantial performance improvements over existing meta-assemblers and single-sample assemblers.
• Achieved significantly higher precision compared to Aletsch, TransMeta, PsiCLASS, Scallop2, and StringTie2.
• Experiments on real and simulated Smart-seq3 scRNA-seq data validated Beaver's superior accuracy.

Conclusions:

• Beaver effectively reconstructs cell-specific full-length transcripts from scRNA-seq data.
• Offers a significant advancement in transcript assembly for single-cell genomics.
• Provides a valuable tool for biological and biomedical research utilizing scRNA-seq.