SARS-CoV-2 variant quantification using kallisto code

doi:10.4121/18532973.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/18532973
Datacite citation style:
Anton, Matei (2022): SARS-CoV-2 variant quantification using kallisto code. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/18532973.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
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.
history
  • 2022-01-26 first online, published, posted
publisher
4TU.ResearchData
format
.py, .sh, .csv, .json, .txt, .fa, .fastq, .tsv, .fasta, .png, .md, .svg
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science

DATA

files (1)