Erdogdu Beril, Varabyou Ales, Hicks Stephanie C, Salzberg Steven L, Pertea Mihaela
Center for Computational Biology, Johns Hopkins University; Baltimore, MD, United States.
Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering; Baltimore, MD, United States.
bioRxiv. 2023 Jul 10:2023.07.10.548289. doi: 10.1101/2023.07.10.548289.
Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and different developmental stages, thereby contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function, potentially leading to pathogenesis of diseases. Detecting such events for single-gene genetic traits is relatively uncomplicated; however, the heterogeneity of populations with complex diseases presents an intricate challenge due to the presence of diverse causal events and undetermined subtypes. SPIT is the first statistical tool that quantifies the heterogeneity in transcript usage within a population and identifies predominant subgroups along with their distinctive sets of DTU events. We provide comprehensive assessments of SPIT's methodology in both single-gene and complex traits and report the results of applying SPIT to analyze brain samples from individuals with schizophrenia. Our analysis reveals previously unreported DTU events in six candidate genes.
差异转录本使用(DTU)在决定基因表达如何在细胞、组织和不同发育阶段之间产生差异方面起着至关重要的作用,从而促成了生物系统的复杂性和多样性。在异常细胞中,它还可能导致蛋白质功能缺陷,进而引发疾病的发病机制。检测单基因遗传性状的此类事件相对简单;然而,由于存在多种因果事件和未确定的亚型,复杂疾病人群的异质性带来了错综复杂的挑战。SPIT是首个量化群体内转录本使用异质性并识别主要亚组及其独特DTU事件集的统计工具。我们对SPIT在单基因和复杂性状方面的方法进行了全面评估,并报告了应用SPIT分析精神分裂症患者脑样本的结果。我们的分析揭示了六个候选基因中以前未报告的DTU事件。