Methods for Evolutionary Genomics Analysis

摘要/摘要 核苷酸测序的持续进展已经导致了包含大的核苷酸序列的数据集的组装。 物种、基因和基因组片段的数量。这些数据的系统基因组分析对于 在理解整个生命树的进化模式方面取得了进展，并发现越来越多的 在实际分析中的应用，需要了解模式如何随时间变化。规模庞大 由于过多的时间和内存， 要求.我们已经开发了许多高影响力的方法和工具，用于分子的比较分析， 序列，我们建议通过开发创新方法， 应对生物基因组学的新挑战。我们将专注于基于模式的机器学习方法， 稀疏约束（SL）应用于基因组学，作为对传统的基于模型的方法的补充， 分子进化和遗传学。在拟议的SL系统发育学（SLiP）框架中，我们将建立 用基因组位点最好地解释感兴趣的生物性状或进化假设的模型，例如 基因、蛋白质和基因组片段，作为模型参数。两个实例的初步结果 应用程序建立了通用SLiP框架的前提和承诺。在一个，SLiP成功地检测到 基因座，其包含在基因组数据集中超过了来自数百个基因组数据集的一致和对比信号。 其他基因座时推断系统发育关系。在另一个例子中，SLiP揭示了基因座和生物学特性。 功能类别，港口收敛序列的进化模式与出现， 在不同的进化谱系中具有相同的特征。在所有这些分析中，SLiP只需要一小部分的 传统方法所需的计算时间和内存，并且能够实现更好的进化对比 更少的假设。因此，SLiP的成功开发将提高其可行性、严谨性， 大规模数据分析的可重复性。它还将通过缩短的分析使大数据分析民主化 时间和相对较小的内存占用，并鼓励开发一种新的方法， 基因组分析。这个框架可以从一个免费的SLiP函数库中访问， 可通过命令行直接使用，也可通过与MEGA集成在图形界面中使用 软件

Summary/Abstract Continuing advances in nucleotide sequencing have resulted in the assembly of datasets containing large numbers of species, genes, and genomic segments. Phylogenomic analyses of these data are essential to progress in understanding evolutionary patterns across the tree of life, and are finding increasing numbers of applications in practical analyses that require understanding of how patterns change over time. The sheer size of phylogenomic datasets limits the practical utility of available methods due to excessive time and memory requirements. We have developed many high impact methods and tools for comparative analysis of molecular sequences, a tradition we propose to continue through this MIRA project by developing innovative methods that address new challenges in phylogenomics. We will focus on pattern-based approaches of machine learning with sparsity constraint (SL) applied to phylogenomics, as a complement to traditional model-based methods in molecular evolution and phylogenetics. In the proposed SL in Phylogenomics (SLiP) framework, we will build models that best explain the biological trait or evolutionary hypothesis of interest, with genomic loci, such as genes, proteins, and genomic segments, serving as model parameters. Preliminary results from two example applications establish the premise and promise of a general SLiP framework. In one, SLiP successfully detected loci whose inclusion in a phylogenomic dataset overtakes a consistent and contrasting signal from hundreds of other loci when inferring phylogenetic relationships. In the other example, SLiP revealed loci and biological functional categories that harbor convergent sequence evolutionary patterns associated with the emergence of the same trait in distinct evolutionary lineages. In all of these analyses, SLiP required only a small fraction of the computational time and memory demanded by traditional methods, and it enabled better evolutionary contrasts with fewer assumptions. Consequently, the successful development of SLiP will improve the feasibility, rigor, and reproducibility of large-scale data analysis. It will also democratize big data analytics via shortened analysis time and a relatively small memory footprint, and encourage the development of a new class of methods for phylogenomic analysis. This framework will be accessed from a free library of SLiP functions, which will be directly useable via command line and available in a graphical interface through integration with the MEGA software.