SeqCompress: An algorithm for biological sequence compression

Abstract

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 Comprehensive Review of Emerging Computational Methods for Gene Identification

“…In gene prediction, the information entropy for a sequence property can also be calculated as an optimized feature for machine learning methods [167,195]. Additionally, other signal processing concepts, such as digital filters [196], data compression algorithms [197], and so on, are often seen in gene prediction since they have a natural link in terms of numeric analysis and pattern recognition. In the future, techniques derived from the information theory will perhaps play more important roles in genome analysis.…”

A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme

“…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].…”

“…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].For example, bzip2 can compress 9.7 MB of data to 2.8 MB (the compression ratio (CR) is significantly higher than 2 bits per base (bpb)). Nevertheless, this is far from satisfactory in terms of compression efficiency.…”

“…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].…”

“…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].…”