Bench marking AmpliDiff for Human Monkeypox, Hiv-1 and Influenza-A

Bachelor thesis (2024)

Authors

K. den Boon Electrical Engineering, Mathematics and Computer Science

Contributors

J.A. Baaijens Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
J. van Bemmelen Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
C.A. Raman Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2024 Kevin den Boon
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Published Date

01-02-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

AmpliDiff provides a method which takes a list of genomes and their lineages, and finds a set of amplicons and their primers in such a way that these amplicons can be used to differentiate between the lineages of a specific virus. While it has been shown that AmpliDiff find results comparable to whole genome sequencing for SARS-CoV-2 when looking at abundance estimations, it is not know how well it performs for other viruses, or what factors of a virus impacts the performance of the amplicons found by AmpliDiff.\
In this paper we will be showing the effectiveness of AmpliDiff on Human monkeypox, HIV-1 and Influenza-A.
By running AmpliDiff for the three viruses mentioned above, we obtain sets of amplicons, which are used to do a lineage abundance estimation. By then comparing the estimation to the know abundance we calculate the Mean Average Error (MAE). This MAE will then be used to compare against the MAE obtained from doing a abundance estimation based on whole genome sequencing.
By comparing the amplicons against whole genome sequencing (wgs), we show that using viruses with longer genomes positively impacts the performance of the amplicons. We also show that the amount of misalignment characters added by the Multiple Sequence Alignemnt (MSA), impacts the required settings for AmpliDiff to find amplicons, and can negatively impact the MAE.
Finally, we show that AmpliDiff can be run, with some minor changes to the code base, on segmented genomes, with performance similar to that of single segment genomes.