DNA Sequence Compression using ERGC and NRGC for Better Compression

Current improvements in computing technology makes it possible and very easy to build genomic sequences from the billions of reads within a minimal cost and time. In this article, we are proposing an algorithm which is analyzing two compression algorithms Efficient Referential Genome Compression algorithm and Novel Referential Genomic Compression algorithm. Both are effective genomic compression algorithms. The proposed algorithm is analyzing their behaviour for different types of DNA sequences as reference and target genome to produce a result which can predict in which circumstances one algorithm outper-forms other and vice versa. The proposed algorithm analyses the inherent structure of target and reference genomes and predicts the better compression algorithm. Then it compresses the target sequence using predicted algorithm. Proposed algorithm's experimental results prove that it works better than all existing algorithms in this field.  Huge sized data leads to many problems during data transfer over Internet. If the data size is huge it creates a lot of burden over the Internet during transmission. It also occupies a huge space in the local systems. One major side effect of huge data is network congestion. Huge transmission cost is another factor which happens due to large size of data. To remediate these problems, data compression is a very essential tool. For the compression of general purpose data, various tools are available in the market. These general-purpose compression algorithms run very effectively on general purpose data which we use in day to day life. But they are not suitable to compress special data such as DNA sequences which has their own specific structures. Due to this, such type of general purpose compression algorithm is a poor choice to compress DNA sequences. To compress the data such as DNA sequences, some compression algorithms which makes use of the specific structures of DNA sequences to compress them more efficiently. The algorithms ERGC and NRGC achieves compression ratios which is better than the current best algorithms. Compression ratio is the ratio of the uncompressed data size to the compressed data size. Related Work Next generation sequencing technique is a revolutionary step in human genome sequencing. Next generation sequencing is a massively parallel deep sequencing technique which was initially developed in 1990. Next generation techniques can process millions of sequences in a parallel process to generate gigabytes of data. It can generate millions or billions of base pairs in a very reasonable time. These billions of base pairs occupies huge space. The average data size generated by Next generation technique remains in gigabytes. Due to the generation of large amount of DNA sequences, it was possible to map human genomes very effectively. This has a major role in the completion of Human Genome Project [32] But because of the generation of large genomic data, it is become very hard to transmit it over the Internet. Transmission of this much huge amount of data is very costly and leads to Network congestion. Also, to store these data, large local storage is required. To remediate all these problems, a very effective compression algorithm became a need of the hour. Various DNA compression algorithms have been proposed to ful ll that need. We will now brie y discuss some of the proposed genomic compression algorithm which uses a reference from the same species. The basic idea of referential genome compression is, we first choose a reference sequence R which can be chosen randomly or algorithmically. All the other sequences are then com-pressed with respect to R. The target sequence T is first aligned to reference R and the mismatches between two are identified and encoded. The most recent algorithm ERGC  Divides both the target and the reference sequences into components of identical size and finds one-to-one maps of similar regions from every part. It then outputs identical maps in conjunction with numerous areas of the goal series. Delta encoding and PPMD  lossless compression algorithm is used to compress the variations between the reference and the target genomes. If the variations between the reference and the target are small, it outperforms all the best-known algorithms. But its performance degrades when the variations are high. GRS [20] is a reference-primarily based genome compression device exclusively dependent on the Unix application diff. GDC [30] is a LZ77-fashion Helix Vol. 8: 1498-1502 1499  1977)  compression algorithm carefully related to RLZopt (Shanika etal., 2011)  in which GDC[30] performs a non-greedy parsing of the target into the reference by means of hashing. On the opposite, RLZopt [18] uses a suffix array. GReEn[16] is also a reference-primarily based genome compression device. It encodes the goal series by use of an arithmetic encoder. GReEn  assumes that the sequences are already aligned and may be outstanding through SNPs  The simple functioning of iDoComp [13][29] may be summarized in three fundamental steps: (i) mapping era: in this stage, the target genome is expressed in terms of the reference genome. It makes use of suffix arrays to parse the target into the reference; (ii) post-processing: the submitprocessing seems for consecutive suits that may be merged collectively and converted into an approximate in shape and (iii) entropy encoding: entropy encoder compresses the mapping and generates the compressed le. Subrata Saha and Sanguthevar Rajasekaran (Department of Computer Science and Engineering, University of Connecticut) have proposed two algorithms for efficient genomic data compression, namely ERGC [17] and NRGC[22]. Other compression algorithms such as GDC[20] , iDoComp[29] etc. are also present in the field but ERGC and NRGC clearly outsmart them in terms of data compression ratio. NRGC is mostly introduced as an improvement over ERGC by the authors. For some cases, it works better than ERGC but not for all cases. NRGC uses preprocessing for better alignment of reference and target genome before applying the compression algorithm. The complexity for the preprocessing stage in NRGC is O (n) where n is the size of reference and/or target genome. For some datasets, this preprocessing actually reduces the overall cost of compression and in those cases NRGC [22] works better than ERGC . But for few cases, this pre-processing increases the overall cost and hence NRGC [22] works poorly as compared to ERGC [17]. Authors did not significantly point out for which type of datasets one algorithm will outperform another algorithm. So, it remains to find out the nature of the datasets for which ERGC will outperform NRGC [22] and for other datasets for which NRGC [22] will outperform ERGC . In this project, we are trying to tackle this problem and are trying to figure out the types of datasets (reference + target pair) for which one algorithm is better than another algorithm.