{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/68180"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/68180","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Computational methods for high-throughput pooled genetic experiments","abstract":"Advances in high-throughput DNA sequencing have created new avenues of attack for classical genetics problems. This thesis develops and applies principled methods for analyzing DNA sequencing data from multiple pools of individual genomes. Theoretical expectations under several genetic models are used to inform specific experimental designs and guide the allocation of experimental resources. A computational framework is developed for analyzing and accurately extracting informative data from DNA sequencing reads obtained from pools of individuals. 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