SARS-CoV-2 variant quantification using kallisto code

doi: 10.4121/18532973.v1
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doi: 10.4121/18532973
Datacite citation style:
Anton, Matei (2022): SARS-CoV-2 variant quantification using kallisto code. Version 1. 4TU.ResearchData. software.
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Code used together with its results for the paper as part of my Bachelor's Thesis project. The research consists of optimizing the kallisto algorithm for predicting the abundances of SARS-CoV-2 variants in wastewater samples. Specifically, I look at how only sequencing certain regions of the genome influences the prediction accuracy of this pipeline.
  • 2022-01-26 first online, published, posted
.py, .sh, .csv, .json, .txt, .fa, .fastq, .tsv, .fasta, .png, .md, .svg
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science


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