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

BMC Bioinformatics

Methodology Article

Jia Meng¹  Yufei Huang²  Shao-Wu Zhang³  Lian Liu³ 

[1] Department of Biological Sciences, HRINU, SUERI, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, Jiangsu, China;Institute of Integrative Biology, University of Liverpool, L7 8TX, Liverpool, UK;Department of Electrical and Computation Engineering, University of Texas at San Antonio, 78230, San Antonio, TX, USA;Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, 710072, Xi’an, China;

关键词: Differential methylation analysis;    mA;    Negative binomial distribution;    RNA methylation;    Small-sample size;   

DOI  :  10.1186/s12859-017-1808-4

 received in 2017-05-26, accepted in 2017-08-22,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAs 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.ResultsWe 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.ConclusionQNB 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.

【 授权许可】

CC BY   
© The Author(s). 2017

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