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

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。目的是基于单细胞测序数据推断系统发生树的后验分布。在其他方法中，贝叶斯推断将被用于推断关于核苷酸替换模型、树拓扑结构、分支长度的参数值。现在已经有许多研究证明，DNA突变和mtDNA异质性与各种疾病的遗传缺陷有关。单细胞组学技术在过去几年中也得到了发展，允许操纵和分析更大的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：）。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，并最终有助于实施靶向治疗。因此，这样的工作的影响，然后可以看到从疾病预防到个人治疗的整个链条。参考文献[1]约瑟夫H卡明和罗伯特R索卡尔。“一种在遗传学中推导分支序列的方法”。In：Evolution（1965），pp. 311-326. [2]Luigi Luca Cavalli-Sforza，Italo Barrai和Anthony WF Edwards。“随机遗传漂变下的人类进化分析”。在：冷泉港定量生物学研讨会。第29卷。北京大学出版社. 1964年，第100页。9-20. [3]Alexei J Drummond和Andrew Rambaut。BEAST：Bayesian evolutionary analysis by sampling trees.In：BMC evolutionary biology 7.1（2007），pp. 1-8. [4]约瑟夫·费森斯坦。“进化树从基因频率和数量性状：寻找最大似然估计”。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

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