Methods for Evolutionary Genomics Analysis

进化基因组学分析方法

• 批准号: 10322021
• 负责人: Sudhir Kumar
• 金额: $ 49.53万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10322021
• 关键词: Address; Benchmarking; Big Data Methods; Biological; Categories; Complement; Computer software; Data Analyses; Data Set; Development; Gene Proteins; Genes; Genomic Segment; Genomics; Goals; Libraries; Life; Machine Learning; Memory; Methods; Modeling; Molecular Analysis; Molecular Evolution; Nucleotides; Pattern; Performance; Phylogenetic Analysis; Reproducibility; Research; Signal Transduction; Techniques; Time; Trees; base; comparative; functional genomics; genomic locus; graphical user interface; improved; innovation; interest; large scale data; programs; tool; trait

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.

摘要/摘要 核苷酸测序的持续进展导致了包含大的数据集组装 物种，基因和基因组段的数量。这些数据的系统基础分析对于 理解整个生命树的进化模式的进展，并正在发现越来越多的 在实践分析中的应用，需要了解模式如何随时间变化。纯粹的大小 系统基因组数据集限制了由于时间和记忆力过多而可用方法的实际实用性 要求。我们开发了许多高影响力和工具来比较分子 序列，我们建议通过开发创新方法来继续通过这个MIRA项目的传统 应对系统基因组学的新挑战。我们将专注于与机器学习的基于模式的方法 适用于系统基础学的稀疏性约束（SL），作为对传统基于模型的方法的补充 分子进化和系统发育学。在拟议的系统基因组学（SLIP）框架中，我们将建立 最能解释具有基因组基因座的生物学性状或进化假设的模型，例如 基因，蛋白质和基因组片段，用作模型参数。两个示例的初步结果 应用程序建立了通用滑移框架的前提和承诺。在一个中，滑倒成功检测到 在系统基因组数据集中的包含的基因座超过了数百个一致且对比的信号 推断系统发育关系时的其他基因座。在另一个示例中，滑移揭示了基因座和生物学 具有与出现有关的功能类别具有收敛序列进化模式 在不同的进化谱系中具有相同的特征。在所有这些分析中，滑移仅需要一小部分 传统方法所需的计算时间和记忆，它启用了更好的进化对比度 假设更少。因此，滑移的成功发展将改善可行性，严格性， 大规模数据分析的可重复性。它还将通过缩短分析来使大数据分析民主化 时间和相对较小的内存足迹，并鼓励开发新的方法 系统基因分析。该框架将从免费的滑动功能库中访问，这将是 可通过命令行直接使用，并在图形接口中通过与Mega集成 软件。