Fragmenting Genome Sequences by Coding Regions to Improve Performance of the AmpliDiff Algorithm for Large Genomes

Bachelor thesis (2024)

Authors

S.M.F. Karskens Electrical Engineering, Mathematics and Computer Science

Contributors

J.A. Baaijens Pattern Recognition and Bioinformatics - EEMCS (mentor)
J. van Bemmelen Pattern Recognition and Bioinformatics - EEMCS (mentor)
C.A. Raman Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2024 Samuel Karskens
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Published Date

02-02-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Abundance estimation with the use of environmental samples has been used during the SARS-CoV-2 pandemic to identify the abundances of different lineages. AmpliDiff, an algorithm that tries to find parts of DNA that can differentiate between different input genomes was used on a SARS-CoV-2 dataset to find these amplicons. The AmpliDiff algorithm was able to run on the SARS-CoV-2 set but seemed infeasible for datasets that contain larger or more complex genomes because of the computational requirements and runtime. We introduce a new pre-processing strategy based on selecting the most differentiable coding regions and show the modifications done to AmpliDiff to make AmpliDiff work following this new method. Based on the results we conclude that the approach is promising but still requires more research to be used optimally.