Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
Department of Computer Science, University of Maryland, College Park, MD, USA.
Genome Biol. 2023 Jul 12;24(1):165. doi: 10.1186/s13059-023-03003-x.
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
检测等位基因失衡在异构体水平上需要考虑推理不确定性，这是由 RNA-seq reads 的多映射引起的。我们提出的方法 SEESAW 使用 Salmon 和 Swish 提供各种分辨率级别的分析，包括基因、异构体，以及通过转录起始位点将异构体聚集到组中。聚集策略增强了具有高不确定性的转录物的信号。当存在异构体水平的等位基因失衡时，SEESAW 方法套件被证明比其他等位基因失衡方法具有更高的功效。我们还引入了一种新的测试方法，用于检测随协变量（如时间）变化的失衡。
