bambu

bambu performs multi-sample transcript discovery and quantification using long-read RNA-Seq data, enabling context-specific expression analysis of known and novel transcripts and genes.

Key Features:

• Machine-learning-based transcript discovery: Bambu identifies novel transcripts by estimating the novel discovery rate, replacing arbitrary per-sample thresholds with a single precision-calibrated parameter.
• Context-aware quantification: Bambu generates sample-specific annotations that improve quantification accuracy for both novel and known transcripts by accounting for inactive isoforms.
• Full-length and unique read count retention: Bambu preserves these counts to enable accurate quantification even when reference annotations contain inactive isoforms.
• Multi-sample analysis: Bambu processes multiple samples simultaneously to produce expression estimates for downstream analyses such as differential gene expression or transcript usage.

Scientific Applications:

• Context-specific transcriptome analysis: Bambu enables quantification tailored to specific biological contexts, addressing limitations of fixed reference annotations that may be incomplete or contain inactive isoforms.
• Repetitive element expression analysis: Bambu quantifies isoforms from repetitive elements such as HERVH-LTR7 retrotransposons in human embryonic stem cells.
• Differential gene expression and transcript usage: Bambu output supports downstream analyses including differential gene expression and transcript usage studies.

Methodology:

Bambu uses machine learning for transcript discovery and estimates the novel discovery rate as a precision-calibrated parameter for identifying novel transcripts.