DNA Sequence Compression Algorithm Research Articles

Enhanced Index Based DNA Sequence Compression Algorithm

Enhanced Index Based DNA Sequence Compression Algorithm

SeqCompress: An algorithm for biological sequence compression

The growth of Next Generation Sequencing technologies presents significant research challenges, specifically to design bioinformatics tools that handle massive amount of data efficiently. Biological sequence data storage cost has become a noticeable proportion of total cost in the generation and analysis. Particularly increase in DNA sequencing rate is significantly outstripping the rate of increase in disk storage capacity, which may go beyond the limit of storage capacity. It is essential to develop algorithms that handle large data sets via better memory management. This article presents a DNA sequence compression algorithm SeqCompress that copes with the space complexity of biological sequences. The algorithm is based on lossless data compression and uses statistical model as well as arithmetic coding to compress DNA sequences. The proposed algorithm is compared with recent specialized compression tools for biological sequences. Experimental results show that proposed algorithm has better compression gain as compared to other existing algorithms.

A hybrid particle swarm optimization based memetic algorithm for DNA sequence compression

Due to the recent advancements in high- throughput sequencing technologies, biomedical research is faced with ever increasing quantities of data, and the storage or transmission of the huge amount of data is one of the concerns. So, we presented a novel hybrid particle swarm optimization based memetic algorithm (HPMA) for DNA sequence compression. In HPMA, within the framework of the memetic algorithm, dynamic comprehensive learning particle swarm optimization method is used for global search, and two adaptive local search operators including center symmetry mutation differential evolution operator and adaptive chaotic search operator work in a cooperative way. HPMA looks for the global optimal code book based on extended approximate repeat vector, by which the DNA sequence will be compressed. Experiments were conducted on 19 high-dimensional functions and 11 real DNA sequences. The results show that HPMA is more competitive in both the performance and scalability, and also attains better compression ability than other representative DNA-specific algorithms on DNA sequence data.

Self-configuration single particle optimizer for DNA sequence compression

This paper presents a novel self-configuration single particle optimizer (SCSPO) for DNA sequence compression. Particularly, SCSPO searches an optimal compression codebook of all unique repeat patterns and then DNA sequences are compressed by replacing the duplicate fragments with the indexes of the corresponding matched code vectors in the codebook. Featured with a crucial self-configuration process, SCSPO optimizes the codebook with no predefined parameter settings required. Experimental results on benchmark numerical functions and real-world DNA sequences demonstrate that SCSPO is capable of attaining better fitness value than many other PSO variants and the proposed DNA sequence compression algorithm based on SCSPO attains encouraging compression performance.

Searching a pattern in compressed DNA sequences

This paper introduces a novel algorithm for DNA sequence compression that makes use of a transformation and statistical properties within the transformed sequence. A word based tagged code is used for identification of end of code. The word based encoder uses frequency distribution for assigning the code of words. The designed compression algorithm is efficient and effective for DNA sequence compression. As a statistical compression method, it is able to search the pattern inside the compressed text which is useful in knowledge discovery. Experiments show that our algorithm is shown to outperform existing compressors on typical DNA sequence datasets.

A Novel Approach for Compressing DNA Sequences Using Semi-Statistical Compressor

In this paper, we present an algorithm for DNA sequence compression that uses a replacement method. The replacement method introduces words and a word-based compression scheme is used for encoding. The encoder uses ranks to assign the code of words. The developed statistical compression algorithm is competent and useful for DNA chain compression. We have experimentally showed that the designed algorithm is better than existing compressors on typical DNA sequence datasets.

An efficient normalized maximum likelihood algorithm for DNA sequence compression

This article presents an efficient algorithm for DNA sequence compression, which achieves the best compression ratios reported over a test set commonly used for evaluating DNA compression programs. The algorithm introduces many refinements to a compression method that combines: (1) encoding by a simple normalized maximum likelihood (NML) model for discrete regression, through reference to preceding approximate matching blocks, (2) encoding by a first order context coding and (3) representing strings in clear, to make efficient use of the redundancy sources in DNA data, under fast execution times. One of the main algorithmic features is the constraint on the matching blocks to include reasonably long contiguous matches, which not only reduces significantly the search time, but also can be used to modify the NML model to exploit the constraint for getting smaller code lengths. The algorithm handles the changing statistics of DNA data in an adaptive way and by predictively encoding the matching pointers it is successful in compressing long approximate matches. Apart from comparison with previous DNA encoding methods, we present compression results for the recently published human genome data.