Cellular Phylogenetics and Evolution

项目总结/摘要 随着测序技术的出现，分子进化和基因组学研究进入了一个激动人心的阶段 技术，使我们能够从一个人的数百个细胞中分析基因组变异。现在，进化论 可以在最高的细胞分辨率下揭示模式和过程。然而，最先进的 基因组重建方法对于细胞测序数据表现不佳，因为基因组序列的数量 由于突变率低和时间跨度短，变异很小。细胞序列比对经常是 高，即，少量变体（列）和大量序列（单元格、行）。一个共同特点 这些高数据集中的一个是由于与单细胞相关的技术挑战而存在测序错误 测序即使是很小的测序错误也会导致推断的细胞遗传学变得不可靠， 错误的下游生物学推论。我们将开发分子进化和 系统发育分析用于研究体细胞和病原体进化。具体来说，我们的目标是 (a)开发一个突变排序和遗传估计（MOPE）框架，以推断高数据遗传 （B）将MOPE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，用于对高数据进行高通量分析。 最终，拟议中的软件和研究开发将推动分子进化和基因组学， 生物信息学和生物医学。新的软件及其源代码将免费提供给研究人员， 教育和培训。

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.