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 核苷酸测序的不断进步已经导致包含大量数据的数据集的组装 物种、基因和基因组片段的数量。这些数据的系统发育分析对于 在理解整个生命树的进化模式方面取得了进展，并且发现越来越多的 实际分析中的应用需要了解模式如何随时间变化。绝对的尺寸 由于过多的时间和内存，系统发育组数据集的数量限制了可用方法的实际效用 要求。我们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在 MIRA 项目中延续这一传统 应对系统基因组学的新挑战。我们将重点关注基于模式的机器学习方法 稀疏约束（SL）应用于系统基因组学，作为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 SL 系统基因组学 (SLiP) 框架中，我们将构建 最能解释感兴趣的生物特征或进化假设的模型，具有基因组位点，例如 基因、蛋白质和基因组片段，作为模型参数。两个例子的初步结果 应用程序建立了通用 SLiP 框架的前提和承诺。其中，SLiP成功检测到 系统发育数据集中包含的位点超过了来自数百个的一致且对比的信号 推断系统发育关系时的其他基因座。在另一个例子中，SLiP 揭示了基因座和生物学 具有与出现相关的趋同序列进化模式的功能类别 不同进化谱系中的相同特征。在所有这些分析中，SLiP 仅需要一小部分 传统方法所需的计算时间和内存，并且它实现了更好的进化对比 假设较少。因此，SLiP的成功开发将提高其可行性、严谨性、 和大规模数据分析的再现性。它还将通过缩短分析使大数据分析民主化 时间和相对较小的内存占用，并鼓励开发一类新的方法 系统发育分析。该框架可以从免费的 SLiP 函数库访问，该库将是 可通过命令行直接使用，并通过与 MEGA 集成在图形界面中使用 软件。