Uncovering the epitranscriptome regulatory codes using machine learning

项目摘要 N6-甲基腺苷（m6 A）是在哺乳动物细胞的mRNA中广泛发现的最丰富的甲基化，其 功能在很大程度上是未知的。最近的研究已经积累了越来越多的有力证据， 参与不同的疾病，如白血病，乳腺癌，肺癌和艾滋病。当m6 A接近 在许多疾病中，m6 A的改变与疾病表型的联系是明显的， 大部分都不见了最近的大多数研究都是由高通量测序技术推动的，例如 MeRIP-seq用于m6 A甲基化的全转录组分析。然而，由于这种固有的局限性， 技术，迫切需要复杂的基于机器学习的算法来解决 以高灵敏度和精确度检测m6 A位点，准确定量m6 A甲基化水平， 预测在疾病和正常条件下受差异影响的m6 A位点以及预测基因 其表达水平受m6 A调节。如果不弥合这些知识差距， 发现m6 A在调节疾病中的作用。为了解决这些问题，我们的目标是 建议是1）建立一个深度学习算法的基础分辨率m6 A网站预测; 2）建立一个基础， 使用分层贝叶斯方法的分辨率m6 A差异位点预测;以及3）确定m6 A- 介导的基因和功能的贝叶斯负二项回归。该研究将采用 深度学习和对抗性学习推理方法，并利用甲基化量化和 第一次获得序列信息。此外，它将采用贝叶斯图形模型为基础的方法， 结合序列和甲基化水平信息。预计开发的算法将具有 在功能研究中的广泛应用，我们计划与我们的合作者密切合作，应用这些 算法在他们的研究卡波西肉瘤相关疱疹病毒（KSHV），这将导致实现 我们的长期目标是在未来的m6 A研究的最终验证和实际医疗应用。

Project Summary N6-methyladenosine (m6A) is the most abundant methylation widely found in mRNAs of mammalian cells whose function is largely unknown. Recent research has accumulated increasingly strong evidence of m6A's involvement in different diseases such as leukemia, breast cancer, lung cancer, and AIDs. While m6A's close involvement in many diseases is apparent, mechanistic evidence linking m6A alterations to disease phenotypes is mostly missing. Most of the recent research is fueled by the high throughput sequencing technologies such as MeRIP-seq for transcriptome-wide profiling of m6A methylation. However, due to the innate limitations of such technologies, sophisticated machine learning based algorithms are urgently needed to address the problem of detecting m6A sites with high sensitivity and precision, accurate quantification of m6A methylation levels and the prediction of m6A sites differentially affected under disease and normal conditions and the prediction of genes whose expression levels are regulated by m6A. Without bridging these knowledge gaps, it is impossible to made inroads to the problem of finding m6A's role in regulating diseases. To address these issues, our aims in this proposal are 1) Establish a deep learning algorithm for base-resolution m6A site prediction; 2) Establish base- resolution m6A differential site prediction using a hierarchical Bayesian approach; and 3) Determine m6A- mediated genes and functions by Bayesian Negative-Binomial regression. The proposed research will employ deep learning and adversarial learned inference methods and utilize both methylation quantification and sequence information for the first time. Also, it will employ Bayesian graphical model-based methods for combining sequence and methylation level information. It is expected that the developed algorithms will have broad applications in functional study, for which we plan to closely work with our collaborators in applying these algorithms in their research of Kaposi's sarcoma-associated herpesvirus (KSHV), which will lead to the fulfillment our long term goal in the eventual validation and practical medical application of m6A research in the future.