Novel bioinformatics methods to detect DNA and RNA modifications using Nanopore long-read sequencing

项目摘要 DNA修饰，如DNA上的5-甲基胞嘧啶（5 mC）和5-羟甲基胞嘧啶（5 hmC），以及 RNA修饰，如mRNA上的N6-甲基腺苷（m6 A），已经涉及基因调控， 人类疾病。合成的碱基类似物，如BrdU、EdU和IdU，已被用作基因组标记， 研究基本的生物过程。然而，检测DNA/RNA修饰的常规方法 依赖于间接读出（如亚硫酸氢盐处理或免疫沉淀），不能测定重复区域（由于 到短读取的使用），并且遭受各种技术偏见。虽然直接DNA/RNA测序的 Oxford Nanopore平台可以解决这些技术限制，迫切需要开发可靠的 生物信息学方法从离子电流数据中检测常见的DNA/RNA甲基化，具有以下能力： 扩展到罕见的修改形式。我们多年来致力于开发计算工具 用于长读取测序数据的信号水平分析。我们开发了NanoMod， 将DNA修改引入复制细胞，DeepMod使用深度神经网络来预测 5 mC直接来自纳米孔测序的离子电流信号。在目前的建议中，我们将：（1）发展 LongReadSum，它将由多线程C++实现，具有不同格式的模块（FASTA， FASTQ、FAST 5、BAM、POD 5等），用于超快质量控制（QC）和来自纳米孔的信号汇总 测序信号总结过程生成用户指定的特征向量， 用于调用修改的其他下游机器学习工具。(2)开发ModDNA，我们将使用 连接主义时态分类（CTC）和变压器，两个神经网络模型，调用修改后的 例如5 mC、5 hmC和6 mA。此外，我们将调整计算管道，以减少 代表性甲基化测序（RRMS）数据，其使得能够在一个单一的基因组中测定人类基因组。 MinION流通池。(3)开发ModRNA，这是一种整合模型，将先前的基因组特征与 从头和基于模型的上下文依赖性特征（例如，基因3'端的富集） RNA m6 A修饰和其他罕见修饰的检测。(4)优化和改进计算 通过基准数据集的工具。我们将从癌症样本中进行纳米孔DNA测序， 来自临床诊断实验室的配对甲基化谱，以及具有或不具有5- 氮杂-2 '-脱氧胞苷（甲基化抑制剂）治疗。我们将对参考进行直接mRNA测序 有或无胃L3/胃L14敲低，或有体外转录，或有或无KSHV的细胞系 感染，改变epitranscriptomic配置文件。成功完成拟议项目将提供一个 通过纳米孔测序检测DNA/RNA修饰的计算工具箱，提供参考 数据集，极大地促进了我们对人类表观基因组的理解， epitranscriptome。

PROJECT SUMMARY DNA modifications, such as 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) on DNA, as well as RNA modifications, such as N6-methyladenosine (m6A) on mRNA, have been implicated in gene regulation and human diseases. Synthetic base analogs, such as BrdU, EdU and IdU, have been used as genomic markers to study fundamental biological processes. However, conventional approaches to detect DNA/RNA modifications rely on indirect readout (such bisulfite treatment or immunoprecipitation), cannot assay repetitive regions (due to the use of short reads), and suffer from various technical biases. While direct DNA/RNA sequencing on the Oxford Nanopore platform can address these technical limitations, there is an urgent need to develop reliable bioinformatics methods to detect common DNA/RNA methylations from ionic current data, with the ability to extend to rare forms of modifications. We have years of dedication to the development of computational tools for signal-level analysis of long-read sequencing data. We developed NanoMod which detects synthetically introduced DNA modifications into replicating cells, and DeepMod which uses a deep neural network to predict 5mC directly from ionic current signals from Nanopore sequencing. In the current proposal, we will: (1) Develop LongReadSum, which will be implemented by multi-threaded C++ with modules for diverse formats (FASTA, FASTQ, FAST5, BAM, POD5, etc), for ultrafast quality control (QC) and signal summarization from Nanopore sequencing. The signal summarization procedure generates user-specified feature vectors that can be used by other downstream machine-learning tools for calling modifications. (2) Develop ModDNA, where we will use connectionist temporal classification (CTC) and transformers, two neural network models, to call modified bases such as 5mC, 5hmC and 6mA. Additionally, we will adapt the computational pipeline to reduced representation methylation sequencing (RRMS) data, which enables assaying a human genome in one single MinION flowcell. (3) Develop ModRNA, an integrative model which combines prior genomic features with context-dependent features (for example, enrichment in 3’ end of genes) for both de novo and model-based detection of RNA m6A modifications and other rare modifications. (4) Validate and improve the computational tools via benchmarking data sets. We will perform Nanopore DNA sequencing from cancer samples with paired methylation profiles from clinical diagnostic labs, as well as mouse reference cell lines with or without 5- Aza-2’-deoxycytidine (methylation inhibitor) treatment. We will perform direct mRNA sequencing on reference cell lines with or without METTL3/METTL14 knockdown, or with in vitro transcription, or with and without KSHV infection which alters epitranscriptomic profiles. Successful completion of the proposed project delivers a computational toolbox for DNA/RNA modification detection via Nanopore sequencing, provide reference datasets to the community, and greatly facilitates our understanding of the human epigenome and epitranscriptome.