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

该项目重点关注三类小RNA：已知的microRNA（miRNAs）的同种型， 作为isomiRs;来自转移RNA（tRNA）的片段，称为tRF;以及，片段- 核糖体RNA（rRNA）衍生的片段，称为rRFs。 IsomiR、tRFs和rRFs具有几个重要的性质，值得详细研究：（1）它们占 一个细胞中80%的小RNA。(2)它们调节信使RNA（mRNA）和前体RNA的丰度。 teins。(3)它们的表达模式取决于细胞“环境”（例如，组织类型、疾病类型）。(4)在胡- 人，他们的表达模式还取决于“个人属性”（例如，性别、遗传祖先、年龄）。 为了从RNA-seq数据中正确地挖掘isomiR、tRF和rRF，理想的工具必须解决几个复杂的问题， 食物因素首先，相同的短序列（例如，tRF）可以来自不同的亲本RNA。这些父母 RNA可以属于相同的亚型（例如，相同tRNA异源受体的不同tRNA异源解码器）或 不同的子类型（例如，来自不同tRNA isoceptor的isodecoders）。第二，许多iso- miR、tRF和rRF也可以在基因组的不相关区域中发现。第三，旁证和/或不完整 miRNA、tRNA和rRNA的拷贝使许多生物体的核基因组成为谜团，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，委员会认为， 理想的工具必须是靶基因组特异性的。 复杂的因素和基因组特异性的需要最近才出现在文献中。作为 因此，迄今为止，大多数现有工具都是通用的，没有考虑到这些复杂情况。不 令人惊讶的是，大多数现有数据库都是使用通用工具建立的。却没有意识到 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 实验并分析数据。反过来，这导致了许多发表的文章，无意中描述 关于不总是isomiR、tRF或rRF的分子的价值不明确的发现。 我们将通过以下方式解决这些差距。在目标1中，我们将构建专门的工具， 每一种RNA类型，并从人类和小鼠RNA-seq数据中准确地挖掘isomiR，tRF和rRF。的 工具将是强大的、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， 并提供对我们已经通过挖掘编译的isomiR、tRF和rRF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，自动识别NIH SRA中新添加的数据集， 分析和注释每个数据集的isomiR、tRF和rRF，并使用新信息更新数据库， 每个月的信息在目标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个框架，并建立一个系统，使他们能够相互交流，并提交 他们的反馈。最后，我们将通过实验验证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.