Detecting differential transcript usage in complex diseases with SPIT.

Autor: Erdogdu B; 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., Varabyou A; Center for Computational Biology, Johns Hopkins University; Baltimore, MD, United States.; Department of Computer Science, Johns Hopkins University; Baltimore, MD, United States., Hicks SC; Center for Computational Biology, Johns Hopkins University; Baltimore, MD, United States.; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA.; Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA., Salzberg SL; 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.; Department of Computer Science, Johns Hopkins University; Baltimore, MD, United States.; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, MD, USA.; Department of Genetic Medicine, Johns Hopkins School of Medicine; Baltimore, MD, United States., Pertea M; 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.; Department of Computer Science, Johns Hopkins University; Baltimore, MD, United States.; Department of Genetic Medicine, Johns Hopkins School of Medicine; Baltimore, MD, United States.
Jazyk: angličtina
Zdroj: BioRxiv : the preprint server for biology [bioRxiv] 2023 Jul 10. Date of Electronic Publication: 2023 Jul 10.
DOI: 10.1101/2023.07.10.548289
Abstrakt: 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.
Databáze: MEDLINE