Bayesian Joint Estimation of Alignment and Phylogeny

比对和系统发育的贝叶斯联合估计

• 批准号: 8116012
• 负责人: Marc A. Suchard
• 金额: $ 29.54万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8116012
• 关键词: Address; Algorithms; Bioinformatics; Biology; Biomedical Research; Communicable Diseases; Comparative Study; Data; Data Set; Educational process of instructing; Evolution; Fostering; Genes; Genetic; Heterogeneity; Human; Joints; Knowledge; Life; Mathematics; Methods; Modeling; Molecular; Molecular Biology; Motivation; Mutate; Organism; Phylogenetic Analysis; Phylogeny; Play; Process; Property; Protein Family; Role; Sequence Alignment; Sequence Analysis; Sequence Homologs; Side; Sister; Specific qualifier value; Taxon; Techniques; Testing; Time; Training; Trees; Variant; Virus; comparative genomics; conditioning; improved; insertion/deletion mutation; interest; life history; markov model; novel; reconstruction; tool; user friendly software

DESCRIPTION (provided by applicant): Phylogenetic reconstruction is an invaluable tool for studying molecular sequences. Starting from a description of how the characters in the sequences mutate over time, the methods attempt to uncover the sequences' relatedness. Common applications range from describing the evolutionary histories of living organisms in evolutionary biology to estimating genetic distances and constructing protein families in molecular biology and bioinformatics. Standard reconstruction methods rely on sequence alignments that specify which characters in the sequences are homologous, deriving from common ancestors. A fundamental difficulty is that sequence alignments are not directly observed; they are inferred properties of the raw sequence data and must be estimated along with the phylogeny. Current tools handle this inference sequentially, first determining a sometimes poor estimate of the alignment and then conditioning on the truth of alignment to reconstruct the phylogeny. This project provides practical tools for end-users to simultaneously infer alignment and phylogeny, side-stepping biases that sequential estimation introduces. The tools assume both a character substitution model and an insertion/deletion (indel) process through which characters are added or removed generating an alignment. Further, these indels supply previously under-utilized information from the data to infer phytogenies. Major advances make this phylo-alignment framework useful for real-life datasets. The framework draws heavily on hidden Markov models, Bayesian computation and clever parameter integration to produce a computationally efficient inference engine. Expert prior knowledge helps inform the indel process. From this, realistic priors enable Bayes factor tests to address if specific indels are shared by descent or are homoplastic, reducing controversy over their value in phylogenetics. Modeling assumptions better reflect the underlying biology. Allowing spatial variation in the indel process provides more accurate phytogenies and alignments. The extensions also provide for heterogeneity tests to identify evolutionary interesting sequence regions. Examples of the methods span all time-scales of evolution, across billions of years to infer early branches in the Tree of Life to matters of months to describe the diversification of rapidly evolving viruses within infected hosts. This project markedly impacts many fields across biomedical research. For example, the project furnishes mathematical and statistical training in bioinformatics which will play a prime role in discovery during the 21st century, and rigorous inference tools employing phylo-alignment deliver improved molecular, comparative studies, a more accurate understanding of human evolution and new perspectives from which to battle infectious diseases.

描述（由申请人提供）：系统发育重建是研究分子序列的宝贵工具。从描述序列中的字符随时间变化的描述，这些方法试图揭示序列的相关性。共同的应用范围从描述进化生物学中活生物体的进化史到估计遗传距离和在分子生物学和生物信息学中构建蛋白质家族。标准重建方法依赖于序列比对，这些序列比对指定序列中的哪些字符是同源的，来自共同祖先。一个根本的困难是未直接观察到序列比对。它们是原始序列数据的推断性能，必须与系统发育一起估计。当前工具顺序处理这种推论，首先确定对齐对齐的有时估计值，然后根据对齐方式进行调整以重建系统发育。该项目为最终用户提供了实用的工具，可以同时推断顺序估计引入的侧向偏差的系统发育和侧向偏差。工具同时假设字符替代模型和插入/删除（Indel）过程，通过该过程添加或删除字符的生成对齐方式。此外，这些indels先前从数据中提供了不足的信息来推断植物发生。重大进步使这个对现实生活数据集有用的Phylo-Alignment框架。该框架在隐藏的马尔可夫模型，贝叶斯计算和巧妙的参数集成上大大借鉴，以产生计算高效的推理引擎。专家的先验知识有助于告知Indel流程。由此，现实的先验使贝叶斯因子测试能够解决特定的indels是通过下降共享还是同质塑料，从而减少了其在系统发育中的价值上的争议。建模假设更好地反映了潜在的生物学。允许在Indel过程中的空间变化提供更准确的植物发生和比对。扩展还提供了异质性测试，以识别进化有趣的序列区域。这些方法的示例涵盖了所有进化的时间尺度，在数十亿年中，将生命树中的早期分支推断为几个月的问题，以描述受感染宿主中快速发展的病毒的多样化。 该项目显着影响生物医学研究的许多领域。例如，该项目提供了生物信息学中的数学和统计培训，这将在21世纪的发现中发挥主要作用，并且采用phylo-Anignment的严格推理工具提供了改进的分子，比较研究，对人类进化和新观点具有更准确的理解，并从中获得了新的观点，从而抗击感染性疾病。