QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

• Main Authors: Lian Liu, Shao-Wu Zhang, Yufei Huang, Jia Meng
• Format: Article
• Language: English
• Published: BMC 2017-08-01
• Series: BMC Bioinformatics
• Subjects: Differential methylation analysis; m6A; Negative binomial distribution; RNA methylation; Small-sample size
• Online Access: http://link.springer.com/article/10.1186/s12859-017-1808-4

Abstract Background As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. Results We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. Conclusion QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc.