Sharif Digital Repository

This thesis is concentrated on some subjects on population genetics. In the rst part we give formulae including the expectation and variance of the height and the length of the ancestral recombination graph (ARG) and the expectation and variance of the number of recombination events and we show that the expectation of the length of the ARG is a linear combination of the expectation of the length of Kingman's coalescent and the expectation of the height of the ARG. Also we show give a relation between the expectation of the ARG and the expectation of the number of recombination events. At the end of this part we show that the ARG comes down from innity in the sense that we can dene it with X0... 

Extensive studies have been performed on the genetic variations involved in common neurodegenerative diseases such as Alzheimer's, macular degeneration, and Parkinson's. In most cases, no specific gene has been identified pointing to a distinct pathogenic pathway, therefore, this study mainly aims to find common genes among aforementioned diseases according to determination of a specific pathogenic protein pathway.In this study, we reached a deep understanding of the function of nervous system and the discovery of causative agents of the diseases by applying the sources of information from genome datasets in bioinformatics analysis. The utilized database comprises the classification of... 

Genome mutations whose effects are not specified pose one of the challenges in identifying genetic diseases. Utilizing wet lab tests to detect the pathogenicity of variants can be time-consuming and fiscally expensive. A rapid and cost-effective solution to this problem is the use of machine learning-based variant effect predictors, which have the ability to determine whether a mutation is pathogenic or not. The objective of this research is to predict the pathogenicity of genome variations. The proposed model exclusively utilizes the protein sequence as its input feature and does not have access to other protein features. The data used to construct the model comprises mutations with... 

Genetic heterogeneity within a tumor, which occurs during cancer evolution, is one of the reasons for treatment failure and increased chances of drug resistance. Cancer cells initially derive from a mutated progenitor cell, resulting in shared mutated genes. Throughout the course of tumor formation and progression, the occurrence of new mutations is possible, leading to the generation of cancer cells with various mutated genes. An appropriate approach is to identify the sequence of mutations that have occurred in the tumor, which can be inferred from single-cell sequencing data. Singlecell data provides valuable information about branching events in the evolution of a cancerous tumor. In...