Structure-aware models and algorithms for reconstructing the evolution and ancestral states of genes and genomes

用于重建基因和基因组的进化和祖先状态的结构感知模型和算法

• 批准号: RGPIN-2017-05552
• 负责人: Ouangraoua, Aida
• 金额: $ 1.46万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757312
• 关键词: Structure; aware; models; algorithms; reconstructing

This is a computational genomics research program aimed at solving algorithmic and computational problems in order to improve our understanding of genome evolution. Currently, most genome evolution reconstruction methods are limited to the use of sequence-conserved portions of genomes, paying less attention to the structure of components. However, current discoveries in the life sciences are shedding new light on the complexity of gene structure and its importance for gene function and evolution. In particular, the secondary structure of non-coding RNAs (ncRNAs) and the architecture of eukaryotic coding genes constitute valuable information for the reconstruction of gene evolution and ancestral states. The long-term objective of this program is to develop an integrative approach for reconstructing gene and genome evolution and ancestral states using non-coding and coding genes, while making use of ncRNA folding structure and gene architecture information. For this proposal, the two five-year objectives focus on developing models and algorithms for reconstructing the evolution and ancestral states of ncRNA secondary structures and coding gene architectures.The methodology to reach these objectives consists in first designing improved gene evolution models that take into account full information about coding gene architecture and ncRNA secondary structure. Second, we will consider various bio-inspired optimization problems under the improved evolutionary models, study their theoretical complexity and design appropriate algorithmic solutions. We will use species trees, gene homology relations and architecture/structure information in an integrative approach to reconstruct evolutionary histories and infer ancestral states. We will develop user-friendly software that will be made available and maintained for the community.The impacts of this program will be, first, the contribution to a better understanding of gene and genome structure evolution and the availability of efficient programs and software for computational analyses of these structures. Second, the program will contribute to the training of highly qualified personnel in a growing, demanding, highly multidisciplinary field. Third, it will yield theories and algorithms that are likely to reveal interesting links with more general problems on multiscale structures, trees and graphs.

这是一项计算基因组学研究计划，旨在解决算法和计算问题，以提高我们对基因组进化的理解。目前，大多数基因组演化重建方法仅限于使用序列保守的基因组部分，对组件结构的关注较少。然而，当前的生命科学发现正在阐明基因结构的复杂性及其对基因功能和进化的重要性。特别是，非编码RNA（NCRNA）的二级结构和真核编码基因的结构构成了重建基因进化和祖先状态的宝贵信息。该程序的长期目标是开发一种使用非编码和编码基因重建基因和基因组进化和祖先状态的综合方法，同时利用NCRNA折叠结构和基因结构信息。对于这项提案，两个五年的目标着重于开发模型和算法，用于重建NCRNA二级结构和编码基因体系结构的进化和祖先状态。实现这些目标的方法首先设计了改进的基因演化模型，这些方法考虑了有关编码基因结构和NCRNA二级结构的完整信息。其次，我们将考虑改进的进化模型下的各种生物启发的优化问题，研究其理论复杂性并设计适当的算法解决方案。我们将在整合方法中使用物种树，基因同源关系和结构/结构信息，以重建进化史并推断祖先状态。我们将开发用于社区的用户友好软件，该软件将为社区提供和维护。首先，该计划的影响将是对基因和基因组结构演变的更好理解的贡献，以及对于这些结构的计算分析的有效程序和软件的可用性。其次，该计划将有助于在成长中，高度多学科的领域中对高素质的人员进行培训。第三，它将产生理论和算法，这些理论和算法可能与多尺度结构，树木和图形上更一般的问题揭示有趣的联系。