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. 2021 Nov 5;22(6):bbab245.
doi: 10.1093/bib/bbab245.

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites

Fuyi Li  1 , Xudong Guo  2 , Peipei Jin  3 , Jinxiang Chen  4 , Dongxu Xiang  5 , Jiangning Song  6 , Lachlan J M Coin  7
Affiliations

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites

Fuyi Li et al. Brief Bioinform. .

Abstract

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

Keywords: RNA pseudouridine sit; ebioinformatics; machine learning; sequence analysis; stacking ensemble learning.

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Figures

Figure 1
Figure 1
The overall framework of Porpoise. There are four major steps, including dataset preparation, feature engineering, model training and optimization and performance evaluation.
Figure 2
Figure 2
Performance comparison of variant XGBoost models trained using 18 different types of features in terms of AUC value.
Figure 3
Figure 3
Performance comparison of different combinations of base classifiers.
Figure 4
Figure 4
Boxplots showing that Strategy 1 significantly outperformed Strategy 2 in terms of MCC score on both (A) benchmark training and (B) independent test datasets.
Figure 5
Figure 5
Top 20 features of Porpoise ranked by the SHAP algorithm for predicting species-specific RNA pseudouridine sites of (A) H. sapiens, (B) S. cerevisiae and (C) M. musculus.
See this image and copyright information in PMC

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