Modeling circRNA expression pattern with integrated sequence and epigenetic features demonstrates the potential involvement of H3K79me2 in circRNA expression

MOTIVATION CircRNAs are an abundant class of noncoding RNAs with widespread, cell/tissue specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. RESULTS The predicted accuracy of the expression status models was high with area under the curve of ROC (AUC) values of 0.89∼0.92 and the false positive rates (FPR) of 0.17∼0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression level prediction models was also good with normalized root-mean-square errors (RMSE) of 0.28∼0.30 and Pearson's correlation coefficient r (PCC) over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33∼0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional 9 cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. AVAILABILITY The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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