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

识别与特定环境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（TRN）是描述转录因子对基因表达调控作用的有效模型，它可以描述不同生物学背景下的基因表达过程。TRN通常是从许多样本的大量基因表达数据中单独或与其他数据类型组合计算构建的。然而，由于批量组学数据集代表样本中“所有”细胞的“平均”分布，因此基于这些数据类型重建的TRN对于研究其效果在单个细胞分辨率或样本包含异质细胞的情况下最佳观察的过程具有有限的价值。因此，这些过程及其TRN的研究应该使用仅参与这些过程的细胞的单细胞组学特征来进行。 ** 然而，目前使用单细胞数据进行TRN重建的方法通常是基于批量数据的方法的对应物，因此忽略了使用这些新技术的数据时存在的挑战和机遇。此外，这些方法对于不同样品的表型标记是不可知的，并且不能鉴定负责感兴趣的表型中的变化的调节机制。我的计划是开发一个统一的计算框架，该框架将允许使用与特定生物表型相关的多组学数据集以单细胞分辨率重建包含（潜在）异质细胞的不同样本的因果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构建。** 这个框架及其计算工具，作为我的研究计划的一部分，将在生物信息学和计算基因组生物学领域产生重大影响，通过提供先进的新方法来识别负责表型结果变异的调控机制。他们将揭示新的生物学机制，并将确定与表型相关的新生物标志物。此外，它们可能揭示操纵生物系统（例如细胞，疾病，生物体）表型特性的新方法：例如提高作物对害虫的抗性或开发神经退行性疾病或癌症的新疗法。这些工具将作为用户友好的公开软件实施，也将使实验生物学家（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，我的计划将通过培养下一代计算生物学家和生物信息学家在本科，研究生（硕士和博士）和博士后的各个层次产生重大影响。

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.