Detecting differential transcript usage in complex diseases with SPIT

Beril Erdogdu; Ales Varabyou; Stephanie C Hicks; Steven L Salzberg; Mihaela Pertea

doi:10.1016/j.crmeth.2024.100736

Detecting differential transcript usage in complex diseases with SPIT

Cell Rep Methods. 2024 Mar 25;4(3):100736. doi: 10.1016/j.crmeth.2024.100736. Epub 2024 Mar 19.

Authors

Beril Erdogdu¹, Ales Varabyou², Stephanie C Hicks³, Steven L Salzberg⁴, Mihaela Pertea⁵

Affiliations

¹ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA. Electronic address: berdogd1@jhu.edu.
² Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
³ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
⁴ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
⁵ Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins School of Medicine and Whiting School of Engineering, Baltimore, MD, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. Electronic address: elapertea@gmail.com.

Abstract

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and developmental stages, contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function and underpin disease pathogenesis. Analyzing DTU via RNA sequencing (RNA-seq) data is vital, but the genetic heterogeneity in populations with complex diseases presents an intricate challenge due to diverse causal events and undetermined subtypes. Although the majority of common diseases in humans are categorized as complex, state-of-the-art DTU analysis methods often overlook this heterogeneity in their models. We therefore developed SPIT, a statistical tool that identifies predominant subgroups in transcript usage within a population along with their distinctive sets of DTU events. This study provides comprehensive assessments of SPIT's methodology and applies it to analyze brain samples from individuals with schizophrenia, revealing previously unreported DTU events in six candidate genes.

Keywords: CP: systems biology; DTU; RNA-seq; alternative splicing; complex diseases; heterogeneity; schizophrenia.

Detecting differential transcript usage in complex diseases with SPIT

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