Using reference-assisted chromosome assemblies to study chromosome structures and evolution in vertebrates

基因组包含编码构建生物体的蛋白质的基因。在进化过程中，基因组发生变化，这些变化通过改变蛋白质形成的时间甚至导致新基因的形成或旧基因的死亡来影响基因。这些事件一起形成了自然选择用来形成新物种或物种适应环境的变异来源之一。基因组的完全测序是指鉴定染色体上沿着的核苷酸序列。为了了解是什么机制驱动不同物种的染色体结构发生变化，以及这如何影响新物种的形成或现有物种对不断变化的环境的适应，我们将使用称为“参考辅助染色体组装”或RACA的新算法重建新测序物种的完整染色体结构。该算法将一个生物体基因组的测序部分与另一个生物体的现有完整染色体组装进行比较，并重建它们假定的共同祖先的染色体。然后，它使用新测序的基因组的一部分，并搜索染色体的祖先组织和为生物体生成的染色体部分所提出的组织之间的差异。在最后一步，它根据序列支架提出的顺序组织部分或祖先染色体。在本提案提出的研究中，我们将开发几种算法，通过观察染色体的具体特征来验证这些重建，例如“端粒”（染色体末端）和“着丝粒”（对细胞分裂很重要）。这些结构包含可以从测序项目期间产生的序列数据重建的特定序列特征。通过检测这些特征在重构染色体中的位置，我们将能够检查重构染色体的结构与感兴趣物种中的真实的染色体有多接近。如果有问题，我们将采用RACA算法来改进装配。在下一步中，我们将使用RACA生成的染色体来研究在DNA水平上驱动染色体变化的机制。我们将检查在我们分析的所有基因组中没有重排的染色体部分的分布是否可以用进化中染色体的随机断裂来解释，或者在脊椎动物基因组的某些部分中是否存在针对染色体重排的选择。如果我们分析一个大的物种集合，我们可能能够找到哺乳动物、灵长类动物或脊椎动物基因组的“积木”，这些基因组不能被重新排列，而不会对生物体产生致命影响。进化断裂点区域是染色体在进化中断裂然后以不同的组合或方向重新连接的区域。我们将使用多个RACA基因组来研究基因组的哪些特征正在驱动这些事件。一个需要回答的重要问题是“哪些基因更有可能受到这些进化事件的影响？“以前我们证明了进化断点区域富含与谱系特异性特征相关的基因。在这个项目中，我们将对这些间隔进行生物信息学分析，试图对发生在某些谱系的祖先基因组中的谱系特异性变化进行分类，这些变化导致了自然选择选择的特定性状的形成，例如，反刍动物瘤胃的形成。我们的假设是，牲畜物种祖先基因组的变化将与这些物种的特征有关，这些特征使它们成为人类有吸引力的蛋白质来源。因此，检测这些祖先的变化是改善这些物种遗传学的重要一步，因为它将为未来的人工选择和品种改良确定最佳基因和其他靶标。

Genomes contain genes that encode proteins that build organisms. In the course of evolution genomes change and these changes affect genes by changing the time when proteins are formed or even leading to formation of new genes or death of old genes. These events together form one of the sources of variations used by the natural selection to form new species or for species adaptation to the environment. Complete sequencing of a genome refers to the identification of the sequence of nucleotides along chromosomes. To understand what mechanisms drive changes in chromosome structures in different species and how this affects formation of new species or an adaptation of existing species to changing environment we will reconstruct complete chromosome structures of newly sequenced species using an novel algorithm called "reference-assisted chromosome assembly" or RACA. This algorithm compares sequenced parts of one organism' genome to existing complete chromosome assembly of another and reconstructs chromosomes of their putative common ancestor. Then it uses parts of the newly sequenced genome and searches for the differences between the ancestral organization of chromosomes and the organization proposed by parts of chromosomes that are generated for the organism. At the final step it organizes parts or ancestral chromosomes according to the order proposed by sequence scaffolds. In the research proposed in this proposal we will develop several algorithms to verify these reconstructions by looking at the specific features of chromosomes like "telomeres" - chromosome ends and "centromeres" - important for cell division. These structures contain specific sequence features that could be reconstructed from the sequence data produced during sequencing projects. By detecting positions of these features in the reconstructed chromosomes we will be able to check how close the structure of reconstructed chromosomes is to real chromosomes in the species of interest. If there are issues, we will adapt the RACA algorithm to improve the assembly. In the next step we will use RACA-generated chromosomes to investigate mechanisms driving chromosomal changes at the DNA level. We will check if the distribution of chromosome parts that are not rearranged in all genomes included in our analysis can be explained by the random breakage of chromosomes in evolution, or if there is a selection against chromosomal rearrangements in some parts of a vertebrate genome. If we analyze a large set of species we might be able to find "built blocks" of mammalian, amniote, or vertebrate genomes that cannot be rearranged without a lethal effect for the organism. Evolutionary breakpoint regions are regions of chromosomes where chromosomes were broken and then rejoined in a different combinations or orientation in evolution. We will use multiple RACA genomes to investigate what features of the genomes are driving these events. An important question to answer is "which genes would more likely be affected by these evolutionary events?" Previously we demonstrated that the evolutionary breakpoint regions are enriched for the genes that are associated with the lineage-specific features. In this project we will perform bioinformatics analysis of these intervals in an attempt to classify lineage-specific changes that happened in ancestral genomes of some lineages leading to the formation of their specific traits chosen by natural selection, e.g., formation of the rumen in ruminant species. Our hypothesis is that the changes in ancestral genomes of the livestock species will be connected to those features of the species that made them attractive source of proteins for humans. Therefore, detection of these ancestral changes is an important step for improving genetics of these species as it will identify best gene and other targets for future artificial selection and breed improvement.