Predicting Pseudouridine Sites with Porpoise

Xudong Guo, Fuyi Li, Jiangning Song

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review


Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.

Original languageEnglish
Title of host publicationComputational Epigenomics and Epitranscriptomics
EditorsPedro H. Oliveira
Place of PublicationNew York NY USA
Number of pages13
ISBN (Electronic)9781071629628
ISBN (Print)9781071629611
Publication statusPublished - 2023

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Machine learning
  • RNA pseudouridine site
  • Sequence analysis
  • Stacking ensemble learning

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