Bilateral BBSRC-NSF/BIO: ABI Innovation: Data-driven hierarchical analysis of de novo transcriptomes

双边 BBSRC-NSF/BIO：ABI 创新：数据驱动的从头转录组分层分析

• 批准号: 1564917
• 负责人: Robert Patro
• 金额: $ 31.06万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564917&HistoricalAwards=false
• 关键词: Bilateral; BBSRC; NSF; BIO; ABI

This project will determine what methods are needed when researchers are studying how genes are expressed in organisms that do not have a completed DNA sequence of the genome. Genes provide the potential for functions an organism can carry out under the right conditions, and genes are expressed to make sure cells can perform the functions they need to live in their environment. Each gene can have a family of expressed forms, and their relationships can be very difficult to sort out without the sequence of the gene itself for comparison, but that sequence is missing for most organisms. The view of gene expression obtained from such experiments is typically fractured, incomplete, and difficult to analyze, the central problem this research addresses. Once reliable analysis methods are worked out, this research will produce software tools that use the methods and are designed to take common errors and missing parts of the data into account. As a check, the reconstructed gene sequences will be compared to known genes in related organisms, and strong relationships will be used to guide predictions of the genes' functions. All predictions will carry with them a number indicating the uncertainty of the information. To ground the research in a biologically interesting question the methods and software will be used to study plants that use an unusual form of energy conversion, C4 photosynthesis, and compare it to the more common form used by plants in order to investigate the different genetic mechanisms, including regulation, used by each type of plant. Predictions will be tested with wet-lab experiments, and analysis methods will then be improved as needed. An on-line community that shares the interests of the researchers will be created and fostered by providing expert advice for carrying out and analyzing similar experiments. This project will consist of the development of a novel collection of methods, and an integrated set of tools, for the analysis of de novo transcriptomes. There are currently a number of tools that aim to tackle different phases of the de novo transcriptome analysis pipeline (e.g. assembly, clustering, expression quantification and differential expression testing), but none of these provide a well-integrated, principled and efficient approach to this difficult challenge. The methods developed and validated in this project will provide a state-of-the-art pipeline for posing and answering a host of relevant biological questions about how transcripts, genes, and functional modules are differentially expressed and regulated; specifically, in the context of organisms for which a reference genome is lacking. The project will result in the development of novel methods for the data-driven clustering of contigs in de novo assemblies. Clustering will be decided on the basis of sequence and expression-level similarities between contigs, accounting for known hallmarks of mis-assemblies to determine contigs that arise from the same underlying transcript. Transcripts will be grouped by predicted, shared exonic structure to discover sub-genic features, genes, and gene families. The result of this process will be a hierarchical model of the transcriptome. New methods for efficient quantification and differential expression analysis of these hierarchical models will be developed, including a methodologically-sound approach for propagating measures of quantification uncertainty into downstream analysis. Finally, these tools will be validated via a large-scale reanalysis of existing de novo transcriptome data targeted at elucidating the identity of genetic regulatory elements involved in C4 photosynthesis. This reanalysis will harness the increased accuracy and efficiency of the methods developed to analyze data from all previous non-model C4 RNA-seq experiments in a single, multi-scale model. The output of the model will be a system for quantitatively prioritizing candidate regulatory elements for detailed molecular investigation. This project also includes broader impact goals that will provide research opportunities to undergraduate students, and which will create an actively-maintained and inclusive online community centered around best practices in de novo transcriptome analysis and experimental design.For further information regarding progress on this project, including the relevant software being developed, please visit https://combine-lab.github.io/txome.

该项目将确定当研究人员研究基因如何在没有完整的基因组DNA序列的生物体中表达时需要什么方法。基因提供了生物体在适当条件下可以执行的功能的潜力，基因的表达确保细胞可以执行它们在环境中生存所需的功能。每个基因都有一个家族的表达形式，如果没有基因本身的序列进行比较，很难理清它们之间的关系，但大多数生物体都缺少这种序列。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，难以分析，本研究解决的中心问题。一旦制定出可靠的分析方法，这项研究将产生使用这些方法的软件工具，这些软件工具的设计将考虑到常见的错误和数据的缺失部分。作为检查，重建的基因序列将与相关生物体中的已知基因进行比较，并使用强关系来指导基因功能的预测。所有的预测都带有一个数字，表示信息的不确定性。为了将研究建立在一个生物学上有趣的问题上，这些方法和软件将用于研究使用一种不寻常的能量转换形式C4光合作用的植物，并将其与植物使用的更常见的形式进行比较，以研究每种植物使用的不同遗传机制，包括调控。预测将通过湿实验室实验进行测试，然后根据需要改进分析方法。将建立一个分享研究人员兴趣的在线社区，并通过为开展和分析类似实验提供专家建议来促进社区的发展。该项目将包括一个新的收集方法的发展，和一套完整的工具，从头转录组的分析。目前有许多工具旨在解决从头转录组分析管道的不同阶段（例如组装，聚类，表达定量和差异表达测试），但这些工具都没有为这一困难的挑战提供一种整合良好，原则性和有效的方法。该项目开发和验证的方法将提供一个最先进的管道，用于提出和回答有关转录本，基因和功能模块如何差异表达和调节的许多相关生物学问题;特别是在缺乏参考基因组的生物体中。 该项目将导致开发新的方法，用于从头组装中重叠群的数据驱动聚类。 聚类将基于重叠群之间的序列和表达水平相似性来决定，考虑到错误组装的已知标志，以确定由相同的基础转录物产生的重叠群。转录本将根据预测的共享外显子结构进行分组，以发现亚基因特征、基因和基因家族。 该过程的结果将是转录组的分层模型。 将开发新的方法，有效的量化和这些层次模型的差异表达分析，包括一个方法学上健全的方法传播量化不确定性的措施到下游分析。 最后，这些工具将通过大规模的重新分析现有的从头转录组数据进行验证，旨在阐明C4光合作用中涉及的遗传调控元件的身份。这种重新分析将利用所开发的方法的更高准确性和效率，以在单个多尺度模型中分析所有先前非模型C4 RNA-seq实验的数据。该模型的输出将是一个系统，用于定量优先候选调控元件进行详细的分子研究。该项目还包括更广泛的影响目标，将为本科生提供研究机会，并将创建一个积极维护和包容性的在线社区，围绕从头转录组分析和实验设计的最佳实践。https://combine-lab.github.io/txome

项目成果

An Efficient, Scalable and Exact Representation of High-Dimensional Color Information Enabled via de Bruijn Graph Search

• DOI: 10.1101/464222
• 发表时间: 2018-11
• 期刊: bioRxiv
• 作者: Fatemeh Almodaresi;Prashant Pandey;Michael Ferdman;Robert C. Johnson;Robert Patro