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

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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