# A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme
###### Abstract  
With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Therefore, to overcome these challenges, compression has become necessary. In this paper, we describe a new reference-free DNA compressor abbreviated as DNAC-SBE. DNAC-SBE is a lossless hybrid compressor that consists of three phases. First, starting from the largest base (Bi), the positions of each Bi are replaced with ones and the positions of other bases that have smaller frequencies than Bi are replaced with zeros. Second, to encode the generated streams, we propose a new single-block encoding scheme (SEB) based on the exploitation of the position of neighboring bits within the block using two different techniques. Finally, the proposed algorithm dynamically assigns the shorter length code to each block. Results show that DNAC-SBE outperforms state-of-the-art compressors and proves its efficiency in terms of special conditions imposed on compressed data, storage space and data transfer rate regardless of the file format or the size of the data. D 20202017 April  
files only one identifier is added in the head of the data, which represents nucleotides or amino acids [16]. From 1982 to the present, the number of nucleotide bases generated in databases has doubled approximately every 18 months and their sizes can range from terabytes (TB) to petabytes (PB) [17]. In June 2019, approximately 329,835,282,370 bases were generated [18]. All of these data are stored in special databases, which have been developed by the scientific community such as the 1000 Genomes Project [6], the International Cancer Genome Project [19] and the ENCODE project [12; 17; 20]. This advancement has caused the production of a vast amount of redundant data at a much higher rate than the older technologies, which will increase in the future and may go beyond the limit of storage and bandwidth capacity [3; 4; 5]. Consequently, all these data pose many challenges for bioinformatics researchers, i.e., storing, sharing, fast-searching and performing operations on this large amount of genomic data become costly, requires enormous space, and has a large computation overhead for the encoding and decoding process [16]. Sometimes the cost of storage exceeds other costs, which means that storage is the primary requirement for unprocessed data [9]. To overcome these challenges in an efficient way, compression may be a perfect solution to the increased storage space issue of DNA sequences [3; 21]. It is required to reduce the storage size and the processing costs, as well as aid in fast searching retrieval information, and increase the transmission speed over the internet with limited bandwidth [13; 22]. In general, compression can be either lossless or lossy; in lossless compression, no information is lost and the original data can be recovered exactly, while in lossy compression, only an approximation of the original data can be recovered [23; 24; 25; 26]. Many researchers believe that lossless compression schemes are particularly needed for biological and medical data, which cannot afford to lose any part of their data [3]. Thus, with looking at the importance of data compression, lossless compression methods are recommended for various DNA file formats such as FASTA and FASTQ file formats. Currently, universal text compression algorithms, including gzip [27] and bzip [28], are not efficient in the compression of genomic data because these algorithms were designed for the compression of English text. Besides, the DNA sequences consist of four bases: two bits should be sufficient to store each base and follow no clear rules like those of text files that cannot provide proper compression results [9; 29].