DNA Sequence Decompression Using Bitmap Matrix & Wavelet Transformation in Image Processing

Author Name(s): Raju Bhukya, B.Vamsi Viswanath, Y.Mahendra Kumar, D. Swathi Kiran, Prashant Bagdia, Girdhar L, Neelesh.G
Author Email: drrajunitw@gmail.com

Abstract

Genomic science is currently encountering an unstable increment of information and quick improvement of sequencing innovation. Till date, various calculations have been created for compacting genomic groupings. The vast majority of which regard genomes as one-dimensional content strings and pack them in view of word references or likelihood models. Some Compression calculations can’t pack DNA successions however just extends in size. This paper proposes a novel approach for genome compression, which changes the genomic successions to double grouping utilizing Genbit Algorithm and develops a grid from the encoded arrangement. On the off chance that the length of the Encoded grouping is not an impeccable square then we attach a few bits to the arrangement to make its length a flawless square. We include paired bits by checking the bit which showed up the most in the encoded grouping. On the off chance that both bits seemed same number of times we pick ‘0’ and connect the bit over and again at its end to make its length an immaculate square for framework development. Further we take the framework as Grayscale Image and Compress by utilizing wavelet change Image Compression Technique. The proposed Compression Algorithm” accomplishes the best compression proportions for whole Genome DNA successions. It is compared with existing ones and is found to accomplish better compression results.

Keywords

Compression, Encode, Decode, DNA Succession

Introduction

The genome is an organism’s complete set of DNA, let in all of its genes. The genome of a living thing comprise all ancestral info encrypt in DNA. Each genome contains all of the info required to physique and hold that living thing. The two fields in deoxyribonucleic acid sequencing which has seen a fast exploitation was speed of processing and inexpensive price which leads to tremendous gain in genomic collection. Future Genesis sequencing(NGS)[4] ,a Broad through put sequence Engineering and Idiosyncratic molecule sequencing[5] do it easy for galactic measure of one-on-one genomes to be processed in a calendar week or less period of time and costs less thanUS$10000[6]. The thought-provoking, wide-open investigation of problems have created the necessity to store and transfer rattling sizable amount of data. The starring problem colligate with vast data depository. For instance, the storehouse of one 2009 human reference genome needs 905 MB using the tar.gz compaction change [7]. This means that we have to expend more or less 30 minutes in downloading it via a communication system of 4Mb/s bandwidth, and about 5 hours for ten such genomes [1]. DNA sequences comprises of four bases {A, C, T, G} and each base (symbol) can be depicted by two bits. Many standardized text compression tools, such as compress (Lempel–Ziv–Welch “LZW”), gzip (Lempel–Ziv “LZ” + Huffman) and bzip2 (Burrows–Wheeler transform “BWT” + Move-to-Front “MTF” + Huffman), can’t be used to constrict these DNA sequences [28]. According to standardized data, the average compaction ratio is 2.185 bpb “bits per base” for compress, 2.271 bpb for gzip, and 2.138 bpb for bzip2 except for “HUMGHCSA” sequence, where the mean compression ratio is 1.729 bpb [29].

The algorithm discussed above gives the best compression ratio over GenBit algorithm. But the problem comes with the Image Compression Algorithm Where the Decompressed Image cannot be brought back to the exact Original Image. The preparation of Matrix from the encoded sequence is a big challenge here as what should be its dimensions in getting the better output. Coming to our future work, we would like to determine the Image Processing Algorithm other than wavelet transform so that the compression ratio can be improved. Determining the dimensions of Matrix instead of a square matrix and retrieving the original Matrix with greater Accuracy from the Compressed Matrix is also a scope for further better results.

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