Specialized Tools and Auto-updatable Scalable Interactive Databases to Study isomiRs, tRFs and rRFs in Human and Mouse

这个项目主要关注三类小RNA：已知的microRNAs(MiRNAs)的异构体 异构体；源自转移RNA(TRNAs)的片段，称为TRFs；以及FRAG- 源自核糖体RNA(RRNA)的片段，称为rRFs。 IsomiR、TRFs和rRFs有几个重要的性质值得详细研究：(1)它们解释了 一个细胞中约80%的小RNA。(2)它们调节信使RNA(MRNAs)和Pro-2的丰度。 泰恩斯。(3)它们的表达模式取决于细胞“背景”(例如，组织类型、疾病类型)。(4)在胡适- 人类，其表达模式还取决于“个人属性”(例如，性别、基因血统、年龄)。 为了从RNA-seq数据中正确地挖掘异构体、TRFs和RRFs，理想的工具必须解决以下几个复杂的问题： 影响因素。首先，相同的短序列(例如，TRF)可能来自不同的亲本RNA。这些父母 RNA可以属于相同的亚型(例如，同一tRNA等受体的不同tRNA等解码器)或 不同的亚型(例如，来自不同tRNA等受体的等解码子)。第二，许多等价位的序列。 MIR、TRFs和rRFs也可以在基因组的无关区域中找到。第三，副词和/或不完整 MiRNAs、tRNAs和rRNAs的拷贝困扰着许多生物的核基因组，包括人类和 老鼠。这些复杂因素的细节是特定于RNA类型和基因组的。因此， 理想的工具必须是特定于目标基因组的。 复杂的因素和对基因组特异性的需求只是最近才出现在文献中。作为一名 结果，到目前为止，大多数可用的工具都是通用的，没有考虑到这些并发症。不 令人惊讶的是，大多数可用的数据库都是使用通用工具构建的。而没有意识到潜在的 缺点，许多研究人员依靠这些工具和数据库提供的信息来设计 对他们的数据进行实验和分析。反过来，这导致了许多发表的文章无意中描述了 关于并非总是同质异构体、TRFs或rRFs的分子的价值不清楚的发现。 我们将按如下方式解决这些差距。在目标1中，我们将构建专门的工具来解决这些特性 并从人类和小鼠的RNA序列数据中准确地挖掘异构体、TRFs和rRFs。这个 工具将是健壮的、独立的和用户友好的。在目标2中，我们将建立专门的数据库来组织 并提供对我们已经通过挖掘汇编的同质异构体、TRFs和RRFs的信息的轻松访问 5万个公共数据集。在目标3中，我们将构建一个系统，将新添加的数据集自动识别到NIH的SRA， 分析和注释每个数据集的isomiR、trf和rrf，并使用新的infor- 每个月都会收到通知。在目标4中，我们将创建描述最佳实践的教育材料，以帮助研究人员 最大限度地受益于这一框架，并建立一个系统，允许他们彼此交互并提交 他们的反馈。最后，我们将通过实验验证选择与乳腺癌转移有关的小RNA。

This project focuses on three categories of small RNAs: the isoforms of microRNAs (miRNAs) that are known as isomiRs; the fragments that are derived from transfer RNAs (tRNAs) and are known as tRFs; and, the frag- ments that are derived from ribosomal RNAs (rRNAs) and are known as rRFs. IsomiR, tRFs, and rRFs have several important properties that warrant their detailed study: (1) They account for ~80% of all small RNAs in a cell. (2) They regulate the abundance of messenger RNAs (mRNAs) and pro- teins. (3) Their expression patterns depend on cellular “context” (e.g., tissue type, disease type). (4) In hu- mans, their expression patterns additionally depend on “personal attributes” (e.g., sex, genetic ancestry, age). To correctly mine isomiRs, tRFs, and rRFs from RNA-seq data the ideal tools must address several compli- cating factors. First, the same short sequence (e.g., tRF) can arise from different parental RNAs. These parental RNAs can belong to the same sub-type (e.g., different tRNA isodecoders of the same tRNA isoacceptor) or different sub-types (e.g., isodecoders from different tRNA isoacceptors). Second, the sequences of many iso- miRs, tRFs, and rRFs can also be found in unrelated regions of the genome. Third, paralogues and/or incomplete copies of miRNAs, tRNAs, and rRNAs riddle the nuclear genomes of many organisms including human and mouse. The details of these complicating factors are specific to the RNA type and to the genome. Consequently, the ideal tools must be target-genome-specific. The complicating factors and the need for genome specificity appeared in the literature only recently. As a result, most available tools to date have been general-purpose and do not account for these complications. Not surprisingly, most available databases were built using general-purpose tools. Without realizing the underlying shortcomings, many researchers relied on the information provided by these tools and databases to design experiments and analyze their data. In turn, this has led to many published articles that unintentionally describe findings of unclear value about molecules that are not always isomiRs, tRFs, or rRFs. We will address these gaps as follows. In Aim 1, we will build specialized tools that address the peculiarities of each RNA type and accurately mine isomiRs, tRFs, and rRFs from human and mouse RNA-seq data. The tools will be robust, self-contained, and user-friendly. In Aim 2, we will build specialized databases to organize and provide easy access to information about isomiRs, tRFs, and rRFs that we have already compiled by mining 50,000 public datasets. In Aim 3, we will build a system that auto-identifies newly-added datasets to NIH’s SRA, profiles and annotates each dataset’s isomiRs, tRFs, and rRFs, and updates the databases with the new infor- mation each month. In Aim 4, we will create educational material describing best practices to help researchers benefit maximally from this framework, and build a system to allow them to interact with one another and submit their feedback. Lastly, we will validate experimentally select small RNAs implicated in breast cancer metastasis.