Developing novel deep-learning based methods for deciphering non-coding gene regulatory code

总结 该项目将贡献来自变形金刚的新颖的预训练DNA双向编码器表示， 名为DNABERT，以及相关的深度学习工具来破译非编码DNA的语言， 将来自快速积累的序列数据的基因调控信息与NLM的遗传 数据库（例如，dbSNP，dbGaP和ClinVar），通过以下方式为科学家和公众健康服务 帮助确定疾病的遗传成分。虽然遗传密码解释了DNA是如何翻译的， 基因转化为蛋白质是普遍的，但决定基因何时以及如何表达的调控密码各不相同。 在不同的细胞类型和生物体中。非编码DNA由于其多义性的存在而具有高度的复杂性 和遥远的语义关系，从语言建模的角度。最近，深度学习方法已经 已经被用于解开基因调控密码，但未能在全球范围内和鲁棒地模拟这种语言 基因组中的特征，特别是在数据稀缺的情况下。为了应对这一挑战，我们提出DNABERT 将DNA建模为一种语言，通过采用来自Transformers的双向编码器表示的想法， （BERT）.基于自然语言处理研究中的最新观察，我们假设预先训练的 基于transformer的神经网络模型提供了一种有前途的，但尚未完全探索的深度学习方法 用于非编码DNA分析中的各种序列预测任务。我们的初步结果显示 人类基因组上的DNABERT在启动子和剪接位点上达到了最先进的性能， 预测任务，在对小的任务特定数据进行容易的微调之后（Ji，Y.等人，2020）。我们提出的目标是 研究是开发DNABERT用于各种序列预测任务，并与现有状态进行基准测试- 最先进的基于深度学习的方法具体目标是：（1）通过调整开发新的深度学习方法 BERT;（2）将提出的深度学习方法应用于专门针对非编码DNA序列分析 和预测;以及（3）通过应用DNABERT来预测和验证功能性非编码遗传变体 预测模型提出的研究的一个主要贡献是开发预训练DNABERT模型 和预测算法，为DNA序列的分析和预测提供了新的强有力的方法。 由于DNABERT的预训练是资源密集型的，我们将提供源代码和预训练模型 在Github上进行未来的学术研究。我们还将开发一个集成的Web服务器，以（1）部署DNABERT 模型，（2）存储识别的序列特征和预测的数据库，以及（3）帮助用户 将DNABERT应用于他们的具体研究问题。我们期待DNABERT能带来新的进步 通过将先进的语言建模视角引入基因， 法规分析。

SUMMARY This project will contribute novel pre-trained DNA Bidirectional Encoder Representations from Transformers, called DNABERT, and associated deep-learning tools to decipher the language of non-coding DNA and facilitate integration of gene regulatory information from rapidly accumulating sequence data with NLM’s genetic databases (for example, dbSNP, dbGaP and ClinVar), which serve both scientists and the public health by helping identify the genetic components of disease. While the genetic code explaining how DNA is translated into proteins is universal, the regulatory code that determines when and how the genes are expressed varies across different cell-types and organisms. Non-coding DNA is highly complex due to the existence of polysemy and distant semantic relationship, from a language modeling perspective. Recently, deep learning methods have been used in unraveling the gene regulatory code, but failed to globally and robustly model such language features in the genome, especially in data-scarce scenarios. To address this challenge, we propose DNABERT to model DNA as a language, by adapting the idea of Bidirectional Encoder Representations from Transformers (BERT). Based on recent observations in natural language processing research, we hypothesize that pre-trained transformer-based neural network model offer a promising, and yet not fully explored, deep learning approach for a variety of sequence prediction tasks in the analysis of non-coding DNA. Our preliminary results showed that DNABERT on the human genome achieved state-of-the-art performance on promoter and splice-site prediction tasks, after easy fine-tuning on small task-specific data (Ji, Y. et al. 2020). The goal of our proposed research is to develop DNABERT for a variety of sequence prediction tasks, and benchmark with existing state- of-the-art deep-learning based methods. Specific aims are (1) develop novel deep-learning methods by adapting BERT; (2) apply the proposed deep-learning methods to specifically target non-coding DNA sequence analyses and predictions; and (3) predict and validate functional non-coding genetic variants by applying DNABERT prediction models. A major contribution of the proposed research is development of pre-trained DNABERT model and prediction algorithms, which present new powerful methods for analyses and predictions of DNA sequences. Since the pre-training of DNABERT is resource-intensive, we will provide the source code and pre-trained model at Github for future academic research. We will also develop an integrated web server to (1) deploy DNABERT model, (2) database to store the identified sequence features and predictions, and (3) tutorials to help users to apply DNABERT to their specific research problems. We anticipate that DNABERT can bring new advancements and insights to the bioinformatics community by bringing advanced language modeling perspective to gene regulation analyses.