Cellular Phylogenetics and Evolution

细胞系统发育学和进化

• 批准号: 10418915
• 负责人: Sayaka Miura
• 金额: $ 33.68万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418915
• 关键词: Bioinformatics; Biological; Cells; Charge; Collection; Computer software; Data; Data Analyses; Data Set; Dimensions; Disease Outbreaks; Evolution; Frequencies; Genetic Recombination; Genome; Genomics; Genotype; Haplotypes; Individual; Joints; Lead; Libraries; Methodology; Methods; Molecular; Molecular Evolution; Mutation; Mutation Analysis; Pattern; Performance; Phase; Phylogenetic Analysis; Phylogenetic Pattern; Phylogeny; Positioning Attribute; Process; Recurrence; Research; Research Personnel; Resolution; Sequence Alignment; Somatic Cell; Source Code; Techniques; Technology; Testing; Time; Training; Training and Education; Translating; Trees; Variant; Vertebral column; frontier; genetic variant; high throughput analysis; indexing; innovation; large datasets; method development; novel; pathogen; programs; reconstruction; research and development; single cell sequencing; software development; tool

Project Summary/Abstract Molecular evolution and genomics research has entered an exciting phase with the advent of sequencing techniques, enabling us to profile genome variation from hundreds of cells from an individual. Now, evolutionary patterns and processes can be revealed at the highest cellular resolution. However, the state-of-the-art phylogeny reconstruction methods perform poorly for cellular sequencing data because the number of genetic variants is small due to a low mutation rate and short time span. Cellular sequence alignments are frequently tall, i.e., a small number of variants (columns) and a large number of sequences (cells, rows). A common feature of these tall datasets is the presence of sequencing error due to technical challenges associated with single-cell sequencing. Even small sequencing errors cause inferred cellular phylogenies to become unreliable and produce erroneous downstream biological inferences. We will develop innovative methods for molecular evolutionary and phylogenetic analysis of tall data for studying somatic and pathogen evolution. Specifically, our aims will be to (a) develop a mutation ordering and phylogeny estimation (MOPE) framework to infer tall data phylogenies accurately and (b) integrate MOPE with traditional phylogenetic methods to further increase the accuracy of evolutionary inferences. We will also (c) develop a library of software for high-throughput analysis of tall data. Ultimately, the proposed software and research developments will advance molecular evolution and genomics, bioinformatics, and biomedicine. New software and its source code will be made available free for research, education, and training.

项目摘要/摘要 分子进化和基因组学研究进入了一个令人兴奋的阶段，随着测序的出现 技术，使我们能够从一个个体的数百个细胞中介绍基因组变异。现在，进化 模式和过程可以以最高的细胞分辨率揭示。但是，最新的 系统发育重建方法对于细胞测序数据的表现较差，因为遗传学数量 由于突变率较低和时间跨度短，变体很小。细胞序列比对经常 高，即少数变体（列）和大量序列（单元，行）。一个共同的功能 这些高的数据集的存在是由于与单细胞相关的技术挑战而存在测序误差 测序。即使是小测序误差也会导致推断的细胞系统发育变得不可靠并产生 错误的下游生物学推论。我们将开发用于分子进化和的创新方法 用于研究躯体和病原体进化的高数据的系统发育分析。具体来说，我们的目标是 （a）开发突变顺序和系统发育估计（MOPE）框架以推断高数据系统发育 准确并（b）与传统的系统发育方法相结合，以进一步提高 进化推论。我们还将（c）开发一个软件库，用于对高数据的高通量分析。 最终，拟议的软件和研究发展将推进分子进化和基因组学， 生物信息学和生物医学。新软件及其源代码将免费用于研究， 教育和培训。