Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

Karthi Balasubramanian; Nithin Nagaraj

4 . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.24.006007 doi: bioRxiv preprint For measuring complexity of short length segments of the nucleotide sequences, we have used Lempel-Ziv (LZ [22] ) and Effort-To-Compress (ETC [37] ) complexity measures. Lempel-Ziv complexity (LZ), a popular and widely used complexity measure, estimates the degree of compressibility of an input sequence. Effort-To-Compress, a more recently proposed complexity measure (by our research group), determines the number of steps required by the Non-Sequential Recursive Pair Substitution Algorithm to compress the input sequence to a constant sequence (or a sequence of zero entropy). It should be noted that both LZ and ETC are complexity measures derived from lossless data compression algorithms (hence we term them as compression-complexity measures). It has been demonstrated that both LZ and ETC outperform Shannon Entropy in characterizing complexity of noisy time series of short length arising out of stochastic (markov) and chaotic systems [37, 39, 40] . Further, ETC consistently performs better than LZ in a number of applications as shown in recently published literature [39] [40] [41] [42] . For details of how to compute LZ and ETC on actual input sequences, we refer the readers to [22, 37, 43] .