Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Karthi Balasubramanian; Nithin Nagaraj

Pandemics such as the ongoing COVID-19 virus that leads to enormous loss of life globally can only be controlled by finding a vaccine or a very effective antiviral treatment. Finding a vaccine or specific antiviral treatment for such global pandemic of virus diseases requires rapid analysis, annotation and evaluation of metagenomic libraries to enable quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable since they are computationally intensive and cannot be easily scaled up as the number of sequences increase. Thus, there is a need for fast alignment-free techniques for sequence analysis [3, 4] . Further, one may have only short segments and/or incomplete fragments of nucleotide sequences to analyze [5] . Information theory and data compression algorithms provide a rich set of mathematical and algorithmic/computational tools to capture essential patterns in data that could be used for matching nucleotide sequences. Genome sequences are inherently described by character strings and hence amenable to mathematical and computational techniques for extracting information. Exactly what information is being sought from such character strings depends on the string itself and the domain as well as the kind of application. Some targets of interest for analyzing genome sequences include: