Could a mathematical application of information theory identify and predict SARS-CoV-2 mutations?
|Title||Could a mathematical application of information theory identify and predict SARS-CoV-2 mutations?|
|Degree of recognition||International|
|Description||The new paper uses information theory to devise a novel method whereby mutations can be both traced and predicted in genomic sequences. This is far from being the first attempt to do this, for DNA sequences have been analyzed through methods built on information theory from the ‘70s onwards.|
The approach used in this study centers around information entropy (IE) spectra, which are created from genomic sequences, and the examination of their mutation dynamics. Importantly, this approach is relevant for any sequence of any genome of any size.
The researchers used a program called GENIES (GENetic Entropy Information Spectrum), custom-built for this project and now available for free to other scientists
|Producer/Author||Dr. Liji Thomas|
Research output: Working paper