小柯机器人

新加坡科技研究局Jonathan Göke团队的最新研究使用Bambu从长读RNA-seq数据对转录本进行了环境相关的定量。相关论文发表在2023年6月12日出版的《自然—方法学》杂志上。

研究人员开发了Bambu，这是一种基于机器学习执行转录本发现的方法，使用长读RNA测序进行特定于感兴趣环境的定量。为了识别新的转录本，Bambu估计了新颖转录本的发现率，该发现率用单个可解释的、精确校准的参数替换每个样本的任意阈值。Bambu保留全长和唯一读取计数，可在存在非活性亚型的情况下进行准确定量。

与现有的转录本发现方法相比，Bambu在不牺牲灵敏度的情况下实现了更高的精度。研究表明，上下文感知注释可以改善新颖和已知转录本的量化。研究人员运用Bambu对人胚胎干细胞中重复HERVH-LTR7反转录转座子亚型进行了定量，证明了其环境特异性转录表达分析的能力。

研究人员表示，大多数转录本定量方法依赖于固定的参考注释。然而转录组是动态的，根据体内环境，这种静态注释包含某些基因的非活性亚型，而对于其他基因则不完整。

附：英文原文

Title: Context-aware transcript quantification from long-read RNA-seq data with Bambu

Author: Chen, Ying, Sim, Andre, Wan, Yuk Kei, Yeo, Keith, Lee, Joseph Jing Xian, Ling, Min Hao, Love, Michael I., Gke, Jonathan

Issue&Volume: 2023-06-12

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. Leveraging long-read RNA-seq data and machine learning, Bambu facilitates accurate transcript discovery and quantification.

DOI: 10.1038/s41592-023-01908-w

Source: https://www.nature.com/articles/s41592-023-01908-w

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex