DeepRetention: A Deep Learning Approach for Intron Retention Detection - Zhenpeng Wu; Jiantao Zheng; Jiashu Liu; Cuixiang Lin; Hong-Dong Li (Big Data Mining and Analytics)
As the least understood mode of alternative splicing, Intron Retention (IR) is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease studies. Existing methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained introns. These metrics are not able to describe the pattern of sequencing depth of intronic reads, which is an intuitive and informative characteristic of retained introns. We hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR detection. Here we present DeepRetention, a novel approach for IR detection by modeling the pattern of sequencing depth of introns. Due to the lack of a gold standard dataset of IR, we first compare DeepRetention with two state-of-the-art methods, i.e. iREAD and IRFinder, on simulated RNA-seq datasets with retained introns. The results show that DeepRetention outperforms these two methods. Next, DeepRetention performs well when it is applied to third-generation long-read RNA-seq data, while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing data. Further, we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease (AD) samples. The differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene network. The parent genes of differential IRs are enriched in AD-related functions. In summary, DeepRetention detects IR from a new angle of view, providing a valuable tool for IR analysis.