An empirical Bayes change-point model for identifying 3′ and 5′ alternative splicing by next-generation RNA sequencing

Jie Zhang, Zhi Wei

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Motivation: Next-generation RNA sequencing (RNA-seq) has been widely used to investigate alternative isoform regulations. Among them, alternative 3′ splice site (SS) and 5′ SS account for more than 30% of all alternative splicing (AS) events in higher eukaryotes. Recent studies have revealed that they play important roles in building complex organisms and have a critical impact on biological functions which could cause disease. Quite a few analytical methods have been developed to facilitate alternative 3′ SS and 5′ SS studies using RNA-seq data. However, these methods have various limitations and their performances may be further improved. Results: We propose an empirical Bayes change-point model to identify alternative 3′ SS and 5′ SS. Compared with previous methods, our approach has several unique merits. First of all, our model does not rely on annotation information. Instead, it provides for the first time a systematic framework to integrate various information when available, in particular the useful junction read information, in order to obtain better performance. Second, we utilize an empirical Bayes model to efficiently pool information across genes to improve detection efficiency. Third, we provide a flexible testing framework in which the user can choose to address different levels of questions, namely, whether alternative 3′ SS or 5′ SS happens, and/or where it happens. Simulation studies and real data application have demonstrated that our method is powerful and accurate.

Original languageEnglish (US)
Pages (from-to)1823-1831
Number of pages9
Issue number12
StatePublished - Jun 15 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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