Developing phylogenetic inference methods using hybrid, continuous and discrete, data, based on single-cell sequencing technologies

基于单细胞测序技术，使用混合、连续和离散数据开发系统发育推断方法

• 批准号: 2442432
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2442432
• 关键词: Developing; phylogenetic; inference; methods; using

The domain of application of this project is in bio-mathematics and the overall project is supervised by the bio-mathematics department of Imperial College, London. The objective is to infer the posterior distributionof phylogenetic trees based on single-cell sequencing data. Among other methods, Bayesian inference will be used to infer parameter values with respect to the nucleotide substitution models, the tree topologies, thebranch lengths.It has now been proven in many studies that DNA mutations and mtDNA heteroplasmies are linked to genetic defects of various diseases. Single-cell omic-technologies have also been developed in the past fewyears that allow manipulation and analysis of larger datasets of DNA sequences [5]. Raw data in our project will be single-cell (sc) sequencing data (e.g. DNA, mRNA, mitochondrial DNA : : :). An ability to infer theevolution of genes based on these observed single-cell sequences can drastically affect our understanding of somatic-DNA diseases, and eventually contribute to put in place targeted therapies. Therefore the impactof such work can then be seen on the whole chain from disease prevention to individual cure.References[1] Joseph H Camin and Robert R Sokal. "A method for deducing branching sequences in phylogeny". In:Evolution (1965), pp. 311-326.[2] Luigi Luca Cavalli-Sforza, Italo Barrai, and Anthony WF Edwards. "Analysis of human evolution underrandom genetic drift". In: Cold Spring Harbor symposia on quantitative biology. Vol. 29. Cold SpringHarbor Laboratory Press. 1964, pp. 9-20.[3] Alexei J Drummond and Andrew Rambaut. "BEAST: Bayesian evolutionary analysis by sampling trees".In: BMC evolutionary biology 7.1 (2007), pp. 1-8.[4] Joseph Felsenstein. "Evolutionary trees from gene frequencies and quantitative characters: finding maximumlikelihood estimates". In: Evolution (1981), pp. 1229-1242.[5] Jeongwoo Lee, Daehee Hwang, et al. "Single-cell multiomics: technologies and data analysis methods".In: Experimental & Molecular Medicine 52.9 (2020), pp. 1428-1442.3

该项目的应用领域是在生物学上，整个项目由伦敦帝国学院的生物数学系监督。目的是根据单细胞测序数据来推断系统发育树的后验分布。除其他方法外，贝叶斯推论将用于推断有关核苷酸替代模型的参数值，树拓扑，分支长度。现在在许多研究中已证明了DNA突变和mtDNA异质体与各种疾病的遗传缺陷有关。在过去的几年中，也已经开发了单细胞的奥元技术，可以操纵和分析较大的DNA序列数据集[5]。我们项目中的原始数据将是单细胞（SC）测序数据（例如DNA，mRNA，线粒体DNA ：：： :)。基于这些观察到的单细胞序列推断基因进化的能力可以极大地影响我们对体细胞-DNA疾病的理解，并最终有助于实现目标疗法。因此，从疾病预防到各个治疗的整个链中，就可以看到这种工作的影响。 “一种推论系统发育中的分支序列的方法”。在：Evolution（1965），第311-326页。[2] Luigi Luca Cavalli-Sforza，Italo Barrai和Anthony WF Edwards。 “人类进化底层遗传漂移的分析”。在：关于定量生物学的冷泉港研讨会。卷。 29。冷弹簧实验室出版社。 1964年，第9-20页。[3] Alexei J Drummond和Andrew Rambaut。 “野兽：通过取样树的贝叶斯进化分析”。约瑟夫·菲尔森斯坦（Joseph Felsenstein）。 “来自基因频率和定量特征的进化树：找到最大的估计估计值”。在：Evolution（1981），第1229-1242页。[5] Jeongwoo Lee，Daehee Hwang等。 “单细胞多组学：技术和数据分析方法”。