ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species

Ruyi Chen, Fuyi Li, Xudong Guo, Yue Bi, Chen Li, Shirui Pan, Lachlan J.M. Coin, Jiangning Song

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)


A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms stateof-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

Original languageEnglish
Article numberbbad170
Number of pages15
JournalBriefings in Bioinformatics
Issue number3
Publication statusPublished - 1 May 2023


  • A-to-I editing
  • ensemble learning
  • feature selection
  • machine learning
  • RNA modification

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