MINE-PGR: Mining public RNA-seq data to identify and annotate long non-coding RNAs in fifteen diverse angiosperms

在过去的十年里，监测单个细胞、组织或整个生物体中基因表达所需的技术得到了巨大的发展。作为一个直接的结果，现在有数以万计的公开数据集提供了植物如何调节其遗传物质的转录以产生表型的快照。为了理解导致表型的转录复杂性，首先需要了解转录组本身的完整组成。除了蛋白质编码RNA和小RNA，最近发现了第三类转录本：长非编码RNA（lncRNA）。LncRNA正在成为影响植物如何响应环境变化（如温度和水分丰度）的关键调控分子。尽管lncRNA具有许多重要的作用，但它们在植物中的注释仍然很少。LncRNA很难单独从基因组序列中预测，并且通常需要大量的转录信息以及处理这些数据的能力。为了克服lncRNA注释和功能分类中的困难，该项目旨在挖掘15种研究最多的模式和农业上重要的植物物种的所有公开可用的转录组数据。将鉴定lncRNA，确定跨物种保守性，并在15个物种中推断推定的功能途径。这三个数据点（鉴定、保护和功能预测）不仅将提供对植物转录组的更全面的看法，还将帮助研究人员研究基因组和表型组之间的复杂关系。该项目启动了一个新的生物信息学和分子生物学培训课程的本科生称为活的植物，以及夏季研究部分，旨在提供一个增强的生物信息学和分子生物学培训的本科生。长链非编码RNA（lncRNA）在真核生物的许多发育途径中发挥作用。在植物中，lncRNA因其在调节对不同环境刺激的反应中的作用而闻名。表征的植物lncRNA在多大程度上代表了全部功能类别仍然是一个悬而未决的问题。此外，植物转录组由lncRNA组成的程度也是未知的，阻碍了新功能类型的发现。这些知识上的差距部分是由于对植物的取样不佳，以及在鉴定工作中缺乏一致适用的方法。该项目旨在通过挖掘100个公开可用的RNA-seq数据库来记录和注释来自15种被子植物的lncRNA库。这种计算策展将需要识别每个物种的lncRNA，然后根据表达、保守性和预测的生物学过程对其进行注释。作为该项目的一部分开发的生物信息学工作流程将使其他尝试在自己的系统中进行类似大规模策展的团队受益。所有数据和元数据将通过CyVerse的数据存储进行传播，并由流行的公共资源（如EPIC-CoGe和BAR的eFP浏览器）提供可视化。总之，该项目的目标是产生一个创新的资源，可访问和综合信息的植物lncRNA，以及信息和工具，以确定lncRNA与重要的生物功能。这个奖项反映了NSF的法定使命，并已被认为是值得支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

The technology necessary to monitor gene expression in a single cell, within a tissue, or across an entire organism has developed tremendously over the past decade. As a direct result, there are now tens of thousands of publicly available data sets providing snapshots of how plants modulate the transcription of their genetic material to produce a phenotype. In order to appreciate the transcriptional complexity leading to phenotype, it is first necessary to understand the full composition of the transcriptome itself. Aside from protein-coding RNAs and small RNAs, a third class of transcript has recently been uncovered: long non-coding RNAs (lncRNAs). LncRNAs are emerging as key regulatory molecules impacting how plants respond to changes in their environment such as temperature and water abundance. Despite their many important roles, lncRNAs remain poorly annotated in plants. LncRNAs are difficult to predict from genomic sequence alone and often require extensive transcriptional information, and the capacity to process that data. To overcome difficulties in lncRNA annotation and functional classification, this project aims to mine all publicly available transcriptomic data for the fifteen most studied model and agriculturally significant plant species. LncRNAs will be identified, cross-species conservation will be determined, and putative functional pathways will be inferred in each of the fifteen species. These three data points (identification, conservation, and functional prediction) will not only provide a more holistic view of plant transcriptomes, but also help researchers studying the complex relationship between genome and phenome. This project initiates a novel bioinformatics and molecular biology training curriculum for undergraduates called LIVE for Plants, as well as a summer research component aimed at delivering an enhanced bioinformatic and molecular biology training to undergraduates. Long non-coding RNAs (lncRNAs) function in numerous developmental pathways in eukaryotes. In plants, lncRNAs are known for their roles in regulating responses to different environmental stimuli. The extent to which characterized plant lncRNAs represent the total suite of functional classes remains an open question. Moreover, the extent to which plant transcriptomes are composed of lncRNAs is also unknown, hindering discovery of new functional types. These gaps in knowledge are in part due to poor sampling in plants, as well as lack of a consistently applied methodology among identification efforts. This project aims to document and annotate lncRNA repertoires from fifteen angiosperms by mining 100 terabases of publicly available RNA-seq data. This computational curation will entail identifying lncRNAs for each species and then annotating them based on expression, conservation, and predicted biological process. The bioinformatics workflow developed as part of this project will benefit other groups attempting similar large-scale curations in their own systems. All data and metadata will be disseminated through CyVerse's Data Store, with visualization provided by popular public resources such as EPIC-CoGe and BAR's eFP Browsers. Taken together, the project aims will yield an innovative resource of accessible and integrated information about plant lncRNAs, as well as the information and tools to identify lncRNAs with important biological functions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.