IEEE/ACM Trans Comput Biol Bioinform. 2018 Mar-Apr;15(2):526-534. doi: 10.1109/TCBB.2015.2403355.
N6-Methyladenosine (mA) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential mA methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction. Comprehensive evaluation of MeTDiff's performance using both simulated and real datasets showed that MeTDiff is much more robust and achieved much higher sensitivity and specificity over exomePeak. The R package "MeTDiff" and additional details are available at: https://github.com/compgenomics/MeTDiff.
N6-甲基腺苷（mA）转录组甲基化是一个令人兴奋的新研究领域，刚刚引起了研究界的关注。我们在本文中提出了 MeTDiff，这是一种从甲基化 RNA 免疫沉淀测序（MeRIP-Seq）数据中预测差异 mA 甲基化位点的新型计算工具。与现有的算法 exomePeak 相比，MeTDiff 的优势在于它明确地对数据中的读取变异进行建模，并且为差异甲基化位点预测设计了更强大的似然比检验。使用模拟和真实数据集对 MeTDiff 的性能进行综合评估表明，MeTDiff 更加稳健，并且在灵敏度和特异性方面均优于 exomePeak。R 包“MeTDiff”和其他详细信息可在以下网址获得：https://github.com/compgenomics/MeTDiff。
