Browsing by Subject "Genetic interactions"
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Item Computational analysis of genetic interaction network structures and gene properties(2017-07) Koch, ElizabethCellular systems are responsible for many complex tasks, such as carrying out cell cycle phases, responding to intra- and extra-cellular conditions, and resolving errors. Through analysis of biological networks, researchers have begun to describe how cells coordinate these processes by means of modularity and between-process connections. However, descriptions of this network-based cellular organization often do not incorporate the diverse characteristics and individual behaviors of the genes that compose it. Knowledge of gene properties and their relationships with biological network evolution is crucial for a complete understanding of cellular function, and investigation in this area can lead to general principles of biology that apply to many species. This dissertation will describe analyses of the Saccharomyces cerevisiae (baker’s yeast) genetic interaction network that connect gene topological behavior with various physical, functional, and evolutionary properties of genes. Genetic interactions occur between paired genes whose simultaneous mutations produce unexpected double-mutant phenotypes, which are indicative of a range of functional relationships. Because genetic interactions can be identified genome-wide in high-throughput experiments, their networks are comprehensive and unbiased representations of function to which we can apply computational methods that search for structure-function relationships. We begin by exploring the association between a set of gene properties and gene genetic interaction (GI) degree. Here, we build a decision tree model that sorts genes based on a set of properties, each of which has a correlation with GI degree, and accurately predicts GI degree. We show that our model, trained on S. cerevisiae, is also accurate for a very distant yeast species, Schizosaccharomyces pombe, demonstrating that the rules governing gene connectivity are well conserved. Finally, we used predictions from the model to identify gene modules that differ between the two yeast species. Next, we further characterize hub genes through an investigation of pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects. Pleiotropy has typically been described by counting organism-level phenotypes, but a characterization based on genetic interactions can capture details about cellular processes that are buffered by the cell and never manifest in single mutant cellular phenotypes. For this analysis, we use frequent item set mining to discover GI modules, which we annotate with high-level processes, and use entropy to measure the functional diversity of each gene’s set of containing modules, thus distinguishing between genes whose functional influence is limited to very few bioprocesses and those whose roles are important for varied cellular functions. We identified a number of gene and protein characteristics that differed between genes with high and low pleiotropy and discuss the implications of these results regarding the nature and evolution of pleiotropy.Item Computational methods to explore chemical and genetic interaction networks for novel human therapies(2013-11) Deshpande, RaameshModel organisms are often used as a test-bed for the development of new genomic technologies and computational approaches. For example, the yeast Saccharomyces cerevisiae was the first eukaryote to have its entire genome sequenced, paving the way for the sequencing of the human genome. Beyond genome sequencing, yeast and other model organisms have been extensively used for reverse genetics technology development. Reverse genetics is a general approach for studying biology where the genome is perturbed in precise ways (e.g. targeted gene deletion), to gain functional information about the perturbed genes from the resulting phenotypic changes. With developments of new genomic technologies, reverse genetics at a genome-wide scale has become a reality. This dissertation focuses on the development of several computational methods for scaling up the reverse genetics experiments in model organisms as well as for exploring the generated genomics data with the ultimate goal of understanding and translating these data for use in applications for human therapeutics. One such method I developed is COMPRESS-GI which compresses the deletion collection by 95% such that the compressed set still remains highly informative for drug discovery analyses. This compression is critical for conducting chemical genomics experiments on natural products available in extremely limited quantities. I also conducted a systematic comparison of different profile similarity measures for genetic interaction networks which was crucial in discovering dot product as one of the most robust similarity measure. Enabled by these methods, we have conducted chemical genomics experiments for more than 10,000 natural products in yeast and now aim to discover therapeutically interesting compounds for human diseases. For this problem and a more general problem of translating and comparing genomic data across species, we developed a computational method neXus. Furthermore, we have started working on applications that could benefit from discovery of large number of drug-targets. One application is discovery of cancer targets using synthetic lethal interactions; however, very few synthetic lethal interactions are known in human so we developed a novel approach of discovering cancer relevant synthetic lethal interactions by translating the wealth of genetic interactions in model organisms to human.Item Developing next-generation methods for scoring CRISPR screening data(2022-04) Ward, HenryMapping the genotype-to-phenotype connection is a central goal of modern biology. CRISPR screening technology powers mapping efforts by identifying relationships between gene knockouts and conditions of interest, such as another gene knockout or the presence of a drug. Quantitative readouts from CRISPR screening experiments, however, are confounded by several different types of biases which impact the efficacy of genotype-to-phenotype mapping efforts. My contributions to the scientific community consist of several computational tools which address bias in CRISPR screening experiments. The first, Orthrus, addresses biases specific to combinatorial screening experiments which knock out multiple genes simultaneously. The second, Orobas, presents an improved scoring method for chemogenomic screening experiments and substantially reduces false positives compared to the existing state-of-the-art method. Lastly, I detail a novel technique called onion normalization for reducing technical bias in large-scale CRISPR screening experiments. Overall, these contributions serve as a methodological bedrock for the construction of improved genotype-to-phenotype maps from CRISPR screening data.