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

这是一个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，大多数基因组进化重建方法仅限于使用基因组的序列保守部分，而较少关注组分的结构。然而，目前生命科学的发现正在揭示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）的二级结构和真核生物编码基因的结构构成了重建基因进化和祖先状态的有价值的信息。** 该计划的长期目标是开发一种综合方法，用于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，同时利用ncRNA折叠结构和基因结构信息。对于这项提案，两个五年目标的重点是开发模型和算法，用于重建ncRNA二级结构和编码基因结构的进化和祖先状态。达到这些目标的方法包括首先设计改进的基因进化模型，考虑到编码基因的架构和ncRNA二级结构的全部信息。其次，我们将考虑各种生物启发优化问题下的改进的进化模型，研究其理论复杂性和设计合适的算法解决方案。我们将使用物种树，基因同源关系和建筑/结构信息的综合方法来重建进化历史和推断祖先的状态。我们将开发用户友好的软件，并将提供给社区维护。*该计划的影响将是，首先，有助于更好地了解基因和基因组结构的进化和有效的程序和软件的可用性，这些结构的计算分析。第二，该计划将有助于在一个不断增长的，高要求的，高度多学科领域的高素质人才的培训。第三，它将产生的理论和算法，可能会揭示有趣的链接与更一般的问题，多尺度结构，树和图形。

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.