University of Twente Student Theses
Pre-Processing Whole-Genome Datasets To Improve The Execution Time Of Selective Sweep Detection Tools
Boer, S. de (2022) Pre-Processing Whole-Genome Datasets To Improve The Execution Time Of Selective Sweep Detection Tools.
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| Abstract: | The analysis of DNA sequence data of a sampled population allows us to determine what mutations provided an organism or virus with an advantage and caused positive selection. By determining accurately, what part of a DNA sequence is responsible for mutating the organism, we can better understand the world around us, allowing us to find solutions for modern world challenges like finding a more efficient COVID-19 vaccine. The detection of selective sweeps, an indicator for recent positive selection, is done with. time consuming software tools. Other research has touched upon filtering techniques to filter out measurement errors in the input data. This research explores whether filtering can be used for input data without errors to speed up execution time. In this paper, ’SNP-processor’, a software tool, that tests filtering techniques for the pre-processing of whole-genome datasets is in- troduced. Three techniques are introduced that speed up execution time up to 1.28 times at the cost of the TPR and accuracy. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/91855 |
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