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、组织内或整个生物体中的基因表达所需的技术取得了巨大的发展。直接的结果是，现在有数万个公开可用的数据集，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，首先有必要了解转录组本身的完整组成。除了编码蛋白质的RNA和小RNA外，最近还发现了第三类转录本：长的非编码RNA(LncRNAs)。LncRNAs正在成为影响植物如何应对环境变化(如温度和水分丰度)的关键调控分子。尽管lncRNAs有许多重要的功能，但在植物中的注释仍然很差。仅从基因组序列很难预测LncRNAs，而且通常需要广泛的转录信息和处理这些数据的能力。为了克服lncRNA注释和功能分类方面的困难，该项目旨在挖掘15个研究最多的模式植物和农业上重要的植物物种的所有公开可用的转录数据。将确定lncRNAs，将确定跨物种保护，并将推断15个物种中的每一个可能的功能途径。这三个数据点(鉴定、保守和功能预测)不仅将提供对植物转录本的更全面的看法，还将帮助研究人员研究基因组和表现组之间的复杂关系。该项目为本科生开设了一个新的生物信息学和分子生物学培训课程，名为LIVE for Plants，以及一个暑期研究部分，旨在向本科生提供增强的生物信息学和分子生物学培训。真核生物中的长非编码RNA(LncRNAs)在多种发育途径中发挥作用。在植物中，lncRNAs因其在调节对不同环境刺激的反应中的作用而闻名。植物lncRNA的特征在多大程度上代表了整个功能类仍然是一个悬而未决的问题。此外，植物转录本在多大程度上由lncRNAs组成也是未知的，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采样较差，以及在鉴定工作中缺乏始终如一的适用方法。该项目旨在通过挖掘100万亿个公开可用的RNA-SEQ数据来记录和注释来自15个被子植物的lncRNA谱系。这种计算管理将需要识别每个物种的lncRNAs，然后根据表达、保守和预测的生物过程对它们进行注释。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试图在自己的系统中进行类似大规模策展的团体受益。所有数据和元数据将通过CyVerse的数据存储发布，并由EPIC-COGE和BAR的EFP浏览器等流行的公共资源提供可视化。综上所述，该项目AIMS将产生一个关于植物LncRNA的可获取和综合信息的创新资源，以及识别具有重要生物功能的LncRNAs的信息和工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.