Due to the advancement of high-throughput sequencing technologies, it is now feasible for sequencing individual genomes in a fast and affordable manner. With the significant increase in the number of individual genomes, compression methods are needed to reduce pressure on data storage as well as enable effective data distribution and management. The compression methods can generally be divided into two classes, namely reference-free methods and reference-based methods. In reference-free methods, redundancies within the target DNA sequence to be compressed are explored. In reference-based methods, redundancies between the target DNA sequence and other reference sequences are identified to achieve compression. This type of method is applicable to population sequences which are highly similar to each other and have a small number of mismatches. Some of the methods can also be applied to partially similar sequences such as chromosome sequences or sequences having evolutionary relationship. The authors highlight recent developments in these methods. In the comparative study, the authors’ simulation results reveal that the selection of a reference sequence is a crucial factor affecting the compression performance. Use of multiple number of reference sequences and enhancement strategies such as reference rewriting are important to achieve a large compression gain.

Application of signal processing for DNA sequence compression

Data compression plays an important role in lesser expensive resource consumption. It is also cumbersome to send a huge amount of data through the network. The data compression techniques help in optimizing physical storage devices as well as it makes easy to transfer the compressed data or file faster through internet or network. Deoxyribonucleic acid or DNA is biological traits presents in human and almost all other living organisms. The DNA contains the genetic information of the living organisms. In this research, various compression algorithms for DNA sequences are studied and compared. The scheme proposed here for compression is the use of a burrows wheeler compression technique. Our proposed algorithm is the modification of the Burrows Wheeler Compression Algorithm (BWCA). The lexicographical sorting of the matrix is replaced by the polynomial base representation of the bases of DNA. This method reduces the time for compression positively. Validation was performed on various datasets that concur the efficiency of the proposed scheme.

Polynomial Based Representation for DNA Sequence Compression and Search

High throughput DNA sequencing instruments significantly decreases sequencing costs and thus produces large volumes of genomic data. Thus for efficient storage and faster transfer of genomic datasets, compression is inevitable. DNA sequences are rarely accessed alone and DNA of same species have high degree of similarity. Thus considering each sequence separately and utilizing intra-similarity alone forcompression fails to achieve impressive compression ratios when compared to direct encoding. Better compression ratios can be achieved if compression is done based on a reference sequence and encoding only the differences. This paper aims to overview various techniques developed so far for DNA sequence compression ranging from the initial general purpose compression algorithms to the latestmulti-referencesequencebasedcompressionalgorithms.

Towards Context-Aware DNA Sequence Compression Algorithms

The invention discloses a gene sequencing data decompression method, a gene sequencing data decompression system and a computer readable medium. The decompression method comprises the following steps:traversing to obtain to-be-decompressed read sequences Rc, and decompressing and reconstructing each to-be-decompressed read sequence Rc into a positive-negative chain type d, a k-bit original gene sequence CS1 and a reversible operation result CS2; using CS1 as initial short string K- mer to obtain a prediction character c corresponding to an adjacent bit through a prediction data model P1, obtaining a prediction character set PS through sliding window iteration, and coding CS2 and PS and then enabling the CS2 and PS to be subjected to reverse operation decryption through an inverse functionof a reversible function; And combining the CS1 and the decryption result to obtain a read sequence R. The method has the advantages of being low in compression rate, short in decompression time andstable in decompression performance, gene data do not need to be accurately compared, high calculation efficiency is achieved, and the higher the prediction accuracy of a prediction data model is, themore repeated character strings in an inverse operation result are, and the lower the compression rate is.

Gene sequencing data decompression method and system and computer readable medium

Abstract Due to the population growth of the organisms at an unpredicted rate, biological sequence data generation keeps on increasing day by day. Rapid technological development leads to the improved version of sequencing machines which makes it to generate massive amount of Genome sequences much easier than before. Biologists engaged in large Genome projects sequenced huge amount of Genome sequences for different organisms and stored in the biological databases everyday which results in data storage crisis in the field of Bioinformatics. Handling of huge volume of Genome data creates a challenge in storage, analysis and transmission. Compression of Genome sequence data is essential to address this challenge. In this paper, a simple but effective compression algorithm for Genome sequences namely DNA Digital Dictionary (D3) is proposed using an efficient encoding scheme for better compression. The proposed algorithm is demonstrated using Genome sequences of different organisms. Experimental results of the proposed algorithm are compared with the state of art tools and recent existing algorithms. It is observed that the proposed algorithm “D3” outperforms the existing methods in terms of compression ratio and compression time. The average compression ratio for the proposed technique is 4:1 and the time complexity is O(n). 

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Design of Unique Auto generated DNA Digital Dictionary using permutations of finite structures for Genome Sequence Compression

Modern Biotechnology produces large amount of genomic data. The explosion of DNA data has given a challenge for understanding genomic structure, the disk storage and computation. It is essential for the development of efficient compression techniques to handle genomic data storage. Data compression is used to store the data in less memory. The properties of DNA sequence offer a chance to build DNA specific compression algorithms. In this paper, a novel compression technique is proposed for genomic data. In the first stage, each base in DNA sequence is converted into binary form using 2-bit encoding system. On the resultant binary string, A Modified run length encoding is applied. The output is compressed again using Huffman encoding technique in second stage. The encoded sequence is converted into ASCII characters. This technique is quite simple and effective.

Kanusu SrinivasaRao

Papers

A novel compression technique for DNA sequence compaction