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生物数学，整个项目由伦敦帝国理工学院生物数学系监督。其目的是根据单细胞测序数据推断系统发育树的后验分布。在其他方法中，贝叶斯推理将被用来推断关于核苷酸替代模型、树的拓扑结构、分支长度的参数值。现在已经在许多研究中证明DNA突变和mtDNA异质性与各种疾病的遗传缺陷有关。在过去的几年里，单细胞基因组技术也得到了发展，可以操作和分析更大的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：：)。根据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，并最终有助于实施有针对性的治疗。因此，这种工作的影响可以从疾病预防到个别治疗的整个链条上看到。“一种推断系统发育中分支序列的方法”。见：进化论(1965)，第311-326页。[2]Luigi Luca Cavalli-Sforza，Italo Barrai和Anthony WF Edwards。《随机遗传漂移下的人类进化分析》。收录：冷泉港数量生物学研讨会。第29卷。冷泉港实验室出版社。1964年，第9-20页。[3]阿列克谢·J·德拉蒙德和安德鲁·兰博。<野兽：抽样树的贝叶斯进化分析>见：BMC进化生物学7.1(2007)，第1-8页[4]约瑟夫·费尔森斯坦。来自基因频率和数量性状的进化树：寻找最大似然估计“。见：进化论(1981)，第1229-1242页[5]李正宇、黄大熙等。《单细胞多组学：技术与数据分析方法》，摘自：《实验与分子医学》第52.9期(2020年)，第1428-1442.3页。