Aletsch

Aletsch assembles full-length transcripts from multiple bulk or single-cell RNA-seq samples using bridging and graph-decomposition algorithms to restore missed junctions and leverage cross-sample supporting information.

Key Features:

• Bridging system: Integrates multiple samples to restore missed junctions in individual samples.
• Graph-decomposition algorithm: Leverages supporting information across multiple samples to guide the decomposition of complex vertices in the assembly graph.
• Transcript scoring: Applies a random forest model with 50 features for scoring assembled transcripts.

Scientific Applications:

• Multiple-sample transcriptome assembly: Reconstructs full-length transcripts from multiple bulk or single-cell RNA-seq samples with higher accuracy than existing meta-assemblers.
• Cross-species and cross-protocol analysis: Demonstrates robust adaptability across different chromosomes, datasets, and species, including data from several RNA-seq protocols.

Methodology:

Aletsch employs a bridging system for cross-sample junction restoration, a graph-decomposition algorithm utilizing supporting information across samples, and a random forest model with 50 features for transcript scoring.