Data-driven hierarchical analysis of de novo transcriptomes

数据驱动的从头转录组分层分析

• 批准号: BB/P011764/1
• 负责人: Julian Hibberd
• 金额: $ 21.16万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP011764%2F1
• 关键词: Data; driven; hierarchical; analysis; de

Recent developments in sequencing technology have made it possible to directly sample sequencing reads from the expressed transcripts present in a population of cells. This has provided an unparalleled view of gene expression, including how expression changes in response to certain stimuli, across different conditions, and between different disease states. Yet, the de novo analysis of this data, wherein the genome of the organism being sequenced is unknown, still poses many technical challenges. The view of gene expression obtained from such experiments is typically fractured, incomplete, and difficult to analyze in the manner one would in a reference-based scenario, where a corresponding genome is available. In this project, a suite of mathematical models and software tools will be developed to help close the gap between reference-based and de novo analysis of gene expression. The developed tools will be able to identify and correct a host of errors in the predicted (assembled) transcripts. The methods that are typically used to assess gene expression from sequencing data will be specialized for the de novo context, by explicitly accounting for the incomplete nature of the data. The recovered genes will be compared to known genes in related organisms, and new methods will be developed that will allow better prediction of the function of the genes discovered. The methods developed herein will estimate measures of uncertainty at each stage of analysis, and will include the measured uncertainty in all resulting conclusions. Finally, these novel methods and tools will be applied to the study of C4 photosynthesis - a highly efficient form of photosynthesis. A large number of sequencing datasets centered around the C4 system have been produced, yet the genetic mechanism underlying this trait is still unknown. The methods developed in this project will be used to improve our understanding of the regulatory elements involved in the C4 photosynthesis pathway. The project will also include the creation and support of an online community where scientists can seek expert advice on transcriptomic methods (including the software developed herein) and experimental design.

测序技术的最新发展已经使得可以从细胞群体中存在的表达的转录物直接采样测序读段。这为基因表达提供了一个无与伦比的视角，包括表达如何在不同条件下以及不同疾病状态下响应某些刺激而变化。然而，这种数据的从头分析，其中被测序的生物体的基因组是未知的，仍然提出了许多技术挑战。从这样的实验中获得的基因表达的观点通常是破碎的，不完整的，并且难以以基于参考的场景中的方式进行分析，其中相应的基因组是可用的。在这个项目中，将开发一套数学模型和软件工具，以帮助缩小基因表达的基于参考的分析和从头分析之间的差距。开发的工具将能够识别和纠正预测（组装）成绩单中的大量错误。通常用于从测序数据评估基因表达的方法将通过明确说明数据的不完整性质而专门用于从头分析。将恢复的基因与相关生物体中的已知基因进行比较，并将开发新的方法，以便更好地预测发现的基因的功能。本文开发的方法将在分析的每个阶段估计不确定性的测量，并将在所有得出的结论中包括测量的不确定性。最后，这些新的方法和工具将被应用于C4光合作用的研究-一种高效的光合作用形式。已经产生了大量以C4系统为中心的测序数据集，但这种特征背后的遗传机制仍然未知。本项目中开发的方法将用于提高我们对C4光合作用途径中所涉及的调控元件的理解。该项目还将包括创建和支持一个在线社区，科学家可以在那里寻求关于转录组学方法（包括本文开发的软件）和实验设计的专家建议。