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.

项目概要/摘要 随着测序的出现，分子进化和基因组学研究进入了一个令人兴奋的阶段 技术，使我们能够分析来自个体的数百个细胞的基因组变异。现在，进化 可以以最高的细胞分辨率揭示模式和过程。然而，最先进的 系统发育重建方法对于细胞测序数据表现不佳，因为遗传数量 由于突变率低且时间跨度短，变异很小。细胞序列比对经常发生 高，即少量变体（列）和大量序列（单元格、行）。一个共同的特点 这些高数据集的一个问题是由于与单细胞相关的技术挑战而存在测序错误 测序。即使很小的测序错误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。我们将开发分子进化和 用于研究体细胞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) 开发突变排序和系统发育估计（MOPE）框架来推断高数据系统发育 (b) 将 MOPE 与传统的系统发育方法相结合，进一步提高 进化论的推论。我们还将 (c) 开发一个用于高数据高通量分析的软件库。 最终，拟议的软件和研究进展将推动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提供以供研究， 教育和培训。