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