A computational framework to unravel gene regulatory mechanisms using single-cell omics data

识别与特定背景或表型结果相关的转录调控机制是一个重要的生物学问题。转录调控网络代表了转录因子对基因的调控作用，已被证明是描述不同生物背景下基因表达程序的有用模型。TRN通常是由大量基因表达数据通过计算构建的，这些数据跨越许多样本，单独或与其他数据类型结合使用。然而，由于大量的基因组数据集代表了样本中所有细胞的平均轮廓，基于这些数据类型重建的TRN对于研究其影响在单个细胞分辨率下最好地观察到的过程或在样本包含异质细胞的场景中的价值有限。因此，对这些过程及其TRN的研究应仅使用参与这些过程的细胞的单细胞组学图谱进行。然而，目前使用单细胞数据重建TRN的方法通常是基于大量数据的方法，因此忽略了使用这些新技术的数据时存在的挑战和机会。此外，这些方法对不同样本的表型标记是不可知的，并且不能识别导致感兴趣表型变化的调控机制。我的计划是开发一个统一的计算框架，允许使用与特定生物表型相关的多组学数据集，在单细胞分辨率下，跨包含(潜在)异质细胞的不同样本重建因果TRN。这个计算框架将基于我们将开发的新型机器学习和图挖掘算法来构建。这个框架及其计算工具是作为我的研究计划的一部分开发的，通过提供先进的新方法来识别导致表型结果变化的调控机制，将在生物信息学和计算基因组生物学领域产生重大影响。他们将揭开新的生物学机制，并识别与表型相关的新生物标记物。此外，它们可能揭示操纵生物系统(如细胞、疾病、有机体)表型特性的新方法：例如，提高作物对虫害的抵抗力，或开发治疗神经退行性疾病或癌症的新方法。这些工具将作为用户友好的公开可用的软件实施，还将使实验生物学家(最终用户)能够分析他们的数据，从中提取新的见解，并为他们的实验确定新的目标。最后，我的项目将通过培养下一代计算生物学家和生物信息学家在不同水平的本科生、研究生(硕士和博士)和博士后产生重大影响。

Identifying transcriptional regulatory mechanisms related to a specific context or a phenotypic outcome is an important biological problem. The transcriptional regulatory networks (TRNs), which represent the regulatory effects of transcription factors on the genes, have proven to be a useful model for describing gene expression programs in different biological contexts. TRNs are usually constructed computationally from bulk gene expression data across many samples, alone or in combination with other data types. However, since bulk omic datasets represent the 'average' profile of 'all' the cells in a sample, TRNs reconstructed based on these data types have limited value to study processes whose effects are best observed at a single cell resolution or scenarios in which samples contain heterogeneous cells. Consequently, the study of these processes and their TRNs should be performed using single-cell omics profiles of only cells involved in these processes. However, current methods for TRN reconstruction using single-cell data are usually counterparts of methods based on bulk data, and as such ignore challenges and opportunities that exist when using data from these new technologies. Moreover, these methods are agnostic to the phenotypic labels of different samples and cannot identify regulatory mechanisms that are responsible for the variation in a phenotype of interest. My plan is to develop a unified computational framework that will allow reconstruction of causal TRNs, across different samples containing (potentially) heterogeneous cells, at a single-cell resolution using multiomics datasets, related to a specific biological phenotype. This computational framework will be constructed based on novel machine learning and graph mining algorithms, which we will develop. This framework and its computational tools, developed as part of my research program, will have a significant impact in the fields of bioinformatics and computational genome biology by providing advanced novel methods for identification of regulatory mechanisms responsible for variation in a phenotypic outcome. They will unravel new biological mechanisms and will identify novel biomarkers related to a phenotype. In addition, they may reveal new ways of manipulating the phenotypic properties of a biological system (e.g. a cell, a disease, an organism): for example to improve the resistance of crops to pests or to develop new treatments for neurodegenerative diseases or cancer. These tools, which will be implemented as user-friendly publicly available software, will also enable experimental biologists (the end users) to analyze their data, extract new insights from it, and identify novel targets for their experiments. Finally, my program will have a significant impact by training the next generation of computational biologists and bioinformaticians at various levels of undergraduate, graduate (Masters and PhD) and postdoctoral.