Leveraging Big Data for Bioinformatic Analysis in Modern Population Genomics

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dc.contributor.advisorOleksyk, Taras K
dc.contributor.authorWolfsberger, Walter
dc.contributor.otherBattistuzzi, Fabia
dc.contributor.otherWestrick, Randal
dc.date.accessioned2024-09-25T17:11:47Z
dc.date.available2024-09-25T17:11:47Z
dc.date.issued2023-01-01
dc.description.abstractThe technological advancements and the cost reduction of genomic sequencing provide novel capabilities to pose and answer biological questions on a grand scale. The initial efforts of establishing genomic references for many species of the globe serve as a foundation for the projects that aim to analyze the populations of said species. This expansion of the number of samples involved in individual studies is often connected with increased infrastructural costs for data storage and analysis. Population genomics methods are expanding on the existing genetic approaches, leveraging our ability to automate big data processing and introducing comparative methods to our collection of scientific instruments. It has extensive applications in animal and wildlife research, conservation, and human population analyses. These unique opportunities are associated with emerging challenges related to the nature of the approaches and their relative novelty in the field. Analysis of a multitude of individuals often increases requirements in terms of bioinformatics expertise and resources. An increase in complexity and data volume means researchers often need access to high-performance computing facilities and specific training to utilize them. The field still grows, with new instruments or tests frequently introduced and outdated approaches depreciating. This work reviews population genomics methods and their application related to wildlife research, conservation, and human populations research, employs them to provide answers in multiple studies, and presents a newly developed analysis suite. The suite is aimed to facilitate reproducible, accessible population genomic testing across various fields of application, seeking to address the growing expertise challenges in the field.
dc.identifier.urihttps://hdl.handle.net/10323/18157
dc.relation.departmentBiological Sciences
dc.subjectComputational Biology
dc.subjectDiversity
dc.subjectGenomics
dc.subjectGenomiic deserts
dc.subjectPopulation Genomics
dc.titleLeveraging Big Data for Bioinformatic Analysis in Modern Population Genomics

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