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: 8302280
• 负责人: Marc A. Suchard
• 金额: $ 29.52万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8302280
• 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（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如何随时间变化开始，这些方法试图揭示序列的相关性。常见的应用范围从进化生物学中描述生物体的进化史到分子生物学和生物信息学中估计遗传距离和构建蛋白质家族。标准的重建方法依赖于序列比对，它指定序列中的哪些字符是同源的，来自共同的祖先。一个根本的困难是不能直接观察到序列比对；它们是原始序列数据的推断属性，必须与系统发育一起估计。目前的工具按顺序处理这种推断，首先确定有时不准确的对齐估计，然后根据对齐的真实性来重建系统发育。该项目为最终用户提供了实用的工具，可以同时推断序列估计引入的排列和系统发育，回避偏差。这些工具假设字符替换模型和插入/删除（indel）过程，通过该过程添加或删除字符，生成对齐。此外，这些指数从数据中提供了以前未充分利用的信息来推断植物发生。主要的进展使这种物种比对框架对现实生活中的数据集有用。该框架大量利用隐马尔可夫模型、贝叶斯计算和巧妙的参数集成来产生计算效率高的推理引擎。专家的先验知识有助于为indel过程提供信息。由此，现实的先验使贝叶斯因子测试能够解决特定的指数是否由血统共享或同质性，从而减少了对其在系统发育中的价值的争议。建模假设能更好地反映潜在的生物学。允许在indel过程中的空间变化提供了更准确的植物生长和排列。扩展还提供了异质性测试，以确定进化感兴趣的序列区域。这些方法的例子跨越了进化的所有时间尺度，从数十亿年的时间推断出生命之树的早期分支，到几个月的时间来描述受感染宿主内快速进化的病毒的多样化。

项目成果

Ancient hybridization and an Irish origin for the modern polar bear matriline.

• DOI: 10.1016/j.cub.2011.05.058
• 发表时间: 2011-08-09
• 期刊: Current biology : CB
• 作者: Edwards CJ;Suchard MA;Lemey P;Welch JJ;Barnes I;Fulton TL;Barnett R;O'Connell TC;Coxon P;Monaghan N;Valdiosera CE;Lorenzen ED;Willerslev E;Baryshnikov GF;Rambaut A;Thomas MG;Bradley DG;Shapiro B
• 通讯作者: Shapiro B

Massive parallelization of serial inference algorithms for a complex generalized linear model.

• DOI: 10.1145/2414416.2414791
• 发表时间: 2013-01
• 期刊: ACM transactions on modeling and computer simulation : a publication of the Association for Computing Machinery
• 作者: Suchard MA;Simpson SE;Zorych I;Ryan P;Madigan D
• 通讯作者: Madigan D

πBUSS: a parallel BEAST/BEAGLE utility for sequence simulation under complex evolutionary scenarios.

• DOI: 10.1186/1471-2105-15-133
• 发表时间: 2014-05-07
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Bielejec F;Lemey P;Carvalho LM;Baele G;Rambaut A;Suchard MA
• 通讯作者: Suchard MA

The deep roots of the rings of life.

• DOI: 10.1093/gbe/evt194
• 发表时间: 2013
• 期刊: Genome biology and evolution
• 影响因子: 3.3
• 作者: Lake JA;Sinsheimer JS
• 通讯作者: Sinsheimer JS

Fitting Birth-Death Processes to Panel Data with Applications to Bacterial DNA Fingerprinting.

• DOI: 10.1214/13-aoas673
• 发表时间: 2013
• 期刊: The annals of applied statistics
• 作者: Doss CR;Suchard MA;Holmes I;Kato-Maeda M;Minin VN
• 通讯作者: Minin VN