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

这个项目将决定当研究人员在没有完整基因组DNA序列的生物体中研究基因如何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，基因的表达是为了确保细胞能够执行它们在环境中生存所需的功能。每个基因都可以有一个表达形式的家族，如果没有基因本身的序列进行比较，它们之间的关系很难理清，但大多数生物体都没有这种序列。从这些实验中获得的基因表达观点通常是断裂的，不完整的，难以分析的，这是本研究解决的核心问题。一旦制定出可靠的分析方法，本研究将产生使用这些方法的软件工具，并设计用于考虑常见错误和数据缺失部分。作为检查，重建的基因序列将与相关生物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，这些方法和软件将被用于研究使用一种不寻常的能量转换形式的植物，C4光合作用，并将其与植物使用的更常见的形式进行比较，以调查每种植物使用的不同遗传机制，包括调节。预测将通过湿实验室实验进行检验，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，为开展和分析类似实验提供专家建议。该项目将包括开发一套新的方法集合和一套集成的工具，用于分析新生转录组。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，表达量化和差异表达测试），但这些工具都没有提供一个完整的，有原则的和有效的方法来应对这一艰巨的挑战。该项目开发和验证的方法将为提出和回答一系列有关转录本、基因和功能模块如何差异表达和调节的相关生物学问题提供最先进的管道；具体来说，在缺乏参考基因组的生物体的背景下。该项目将导致开发新的方法，用于数据驱动的组合在从头组装中聚类。聚类将根据序列和表达水平上的相似性来决定，考虑到已知的错误组装的特征，以确定来自相同底层转录物的contigs。转录本将按预测的、共享的外显子结构分组，以发现亚基因特征、基因和基因家族。这一过程的结果将是转录组的层次模型。将开发对这些层次模型进行有效量化和差异表达分析的新方法，包括将量化不确定性测量传播到下游分析的方法学上合理的方法。最后，这些工具将通过大规模重新分析现有的从头转录组数据来验证，旨在阐明C4光合作用中涉及的遗传调控元件的身份。这种再分析将利用提高的准确性和效率的方法开发分析数据从所有以前的非模型C4 RNA-seq实验在一个单一的，多尺度模型。该模型的输出将是一个系统，用于定量确定候选调控元件的优先级，以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，以从头转录组分析和实验设计的最佳实践为中心。有关该项目的进一步信息，包括正在开发的相关软件，请访问https://combine-lab.github.io/txome。

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.