Selective Run-Length Constrained Encoding Scheme on Extended Nucleic Acid Memory

The progress of mankind into the technological revolution and rapid digitalization has caused a sharp rise in data harvesting as a global phenomenon. Human life nowadays has moved into a data-driven society where everyday problems and their solutions are dependent upon voluminous data. This, in turn, has increased the requirement of data storage technology. At this juncture, nucleic acid memory had been proposed as an alternate source of data storage and management technology that possessed great data density and high retentivity capabilities. The proposed work exploits the capability of extended nucleotides to enhance the data density of nucleic acid memory and introduces a lossless data compression algorithm named, "Selective Run-Length Constrained Encoding", which effectively accounts for the constraints related to GC content and homopolymer run. This paper also provides an elaborate comparative analysis with numerous works previously proposed in the research literature to provide a descriptive analysis in support of the proposed work.