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DOI: 10.4121/18532973

Anton, Matei (2022): SARS-CoV-2 variant quantification using kallisto code. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/18532973.v1

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Categories

• Other Information and Computing Sciences
• Medical Microbiology

Keywords

Covid-19 kallisto SARS-CoV-2 Wastewater monitoring

Licence

MIT

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by Matei Anton

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

References

• http://resolver.tudelft.nl/uuid%3A990e42c3-e79f-4ff9-85ff-a614554269bb

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science