2-AIN-506, 2-AIN-252: Seminar in Bioinformatics (2), (4)
Summer 2026
Abstrakt

Ying Chen, Andre Sim, Yuk Kei Wan, Keith Yeo, Joseph Jing Xian Lee, Min Hao Ling, Michael I. Love, Jonathan Goke. Context-aware transcript quantification from long-read RNA-seq data with Bambu. Nature methods, 20(8):1187-1195. 2023.

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Download from publisher: https://doi.org/10.1038/s41592-023-01908-w PubMed

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Abstract:

Most approaches to transcript quantification rely on fixed reference annotations; 
however, the transcriptome is dynamic and depending on the context, such static 
annotations contain inactive isoforms for some genes, whereas they are incomplete 
for others. Here we present Bambu, a method that performs machine-learning-based 
transcript discovery to enable quantification specific to the context of interest 
using long-read RNA-sequencing. To identify novel transcripts, Bambu estimates 
the novel discovery rate, which replaces arbitrary per-sample thresholds with a 
single, interpretable, precision-calibrated parameter. Bambu retains the 
full-length and unique read counts, enabling accurate quantification in presence 
of inactive isoforms. Compared to existing methods for transcript discovery, 
Bambu achieves greater precision without sacrificing sensitivity. We show that 
context-aware annotations improve quantification for both novel and known 
transcripts. We apply Bambu to quantify isoforms from repetitive HERVH-LTR7 
retrotransposons in human embryonic stem cells, demonstrating the ability for 
context-specific transcript expression analysis.