Cellular Phylogenetics and Evolution

项目摘要/摘要 随着测序的出现，分子进化和基因组研究进入了一个令人兴奋的阶段 技术，使我们能够描绘一个人数百个细胞的基因组变异。现在，进化 可以在最高的细胞分辨率下显示图案和过程。然而，最先进的 对于细胞测序数据，系统发育重建方法表现不佳，因为遗传基因的数量 由于突变率低和时间跨度短，变异体很小。细胞序列比对经常是 高，即少量的变体(列)和大量的序列(单元格、行)。一个共同的特征 在这些庞大的数据集中，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系统学变得不可靠并产生 错误的下游生物推断。我们将开发创新的分子进化方法和 用于研究体细胞和病原菌进化的TALL数据的系统发育分析。具体地说，我们的目标是 (A)开发突变排序和系统发展估计(MOPE)框架，以推断高层数据系统发展 准确和(B)将MOPE与传统系统发育方法相结合，进一步提高研究的准确性 进化推论。我们还将(C)开发一个软件库，用于高通量数据分析。 最终，拟议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.