DRME: Count-based differential RNA methylation analysis at small sample size scenario

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• 作者：Lian Liu^a ; Shao-Wu Zhang^a ; ^{zhangsw@nwpu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Fan Gao^b ; Yixin Zhang^c ; Yufei Huang^d ; Runsheng Chen^a ; ^e ; Jia Meng^f ; ^{jia.meng@xjtlu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：N6-Methyladenosine (m6A) ; RNA methylation ; MeRIP-Seq ; Differential methylation ; R/Bioconductor package ; Negative binomial distribution
• 刊名：Analytical Biochemistry
• 出版年：2016
• 出版时间：15 April 2016
• 年：2016
• 卷：499
• 期：Complete
• 页码：15-23
• 全文大小：1635 K

文摘

Differential methylation, which concerns difference in the degree of epigenetic regulation via methylation between two conditions, has been formulated as a beta or beta-binomial distribution to address the within-group biological variability in sequencing data. However, a beta or beta-binomial model is usually difficult to infer at small sample size scenario with discrete reads count in sequencing data. On the other hand, as an emerging research field, RNA methylation has drawn more and more attention recently, and the differential analysis of RNA methylation is significantly different from that of DNA methylation due to the impact of transcriptional regulation. We developed DRME to better address the differential RNA methylation problem. The proposed model can effectively describe within-group biological variability at small sample size scenario and handles the impact of transcriptional regulation on RNA methylation. We tested the newly developed DRME algorithm on simulated and 4 MeRIP-Seq case–control studies and compared it with Fisher's exact test. It is in principle widely applicable to several other RNA-related data types as well, including RNA Bisulfite sequencing and PAR–CLIP. The code together with an MeRIP-Seq dataset is available online (https://github.com/lzcyzm/DRME) for evaluation and reproduction of the figures shown in this article.