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Related Experiment Video

Updated: Sep 29, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Use of DFT Distance Metrics for Classification of SARS-CoV-2 Genomes.

Micah Thornton¹, Monnie Mcgee¹

¹Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|March 24, 2022

Summary

Fourier coefficients (FCs) efficiently summarize viral sequence data. This method classifies SARS-CoV-2 sequences by geographic origin, offering a compact and mathematically tractable alternative to sequence strings.

Keywords:

Fourier transform alignment-free methods genomic sequences supervised learning visualization of high-dimensional data

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Area of Science:

Virology
Bioinformatics
Computational Biology

Background:

Viral sequences contain valuable information for epidemiological studies.
Traditional sequence analysis methods can be computationally intensive and require significant storage.
Developing efficient methods for viral sequence analysis is crucial for rapid response to outbreaks.

Purpose of the Study:

To investigate the utility of Fourier coefficients (FCs) for capturing information from viral sequences.
To develop a computationally efficient and compact method for viral sequence analysis.
To classify SARS-CoV-2 sequences based on geographic submission location using FCs.

Main Methods:

Extraction of geographic submission location from SARS-CoV-2 sequence headers from the GISAID Initiative.
Calculation of Fourier coefficients (FCs) corresponding to the extracted geographic information.
Classification of viral sequences using the calculated FCs.

Main Results:

FCs serve as effective numerical summaries for viral sequences.
FCs enable manipulation, identification, and differentiation of sequences using mathematical and statistical methods.
Subsets of FCs can be used, reducing storage requirements while maintaining classification accuracy.

Conclusions:

FCs offer a computationally efficient and compact approach for viral sequence analysis.
This method facilitates classification and differentiation of viral sequences based on geographic origin.
Future research can explore other series transforms for genomic data analysis.