scholarly journals Bit Reduction based Compression Algorithm for DNA Sequences

2021 ◽  
pp. 270-277
Author(s):  
Rosario Gilmary ◽  
Murugesan G
Keyword(s):  
Dna Sequence ◽  
Data Storage ◽  
Compression Ratio ◽  
Dna Sequences ◽  
Genomic Data ◽  
Living Organism ◽  
Biological Sequence ◽  
Compression Algorithms ◽  
Living Organisms ◽  
Abundant Data

Deoxyribonucleic acid called DNA is the smallest fundamental unit that bears the genetic instructions of a living organism. It is used in the up growth and functioning of all known living organisms. Current DNA sequencing equipment creates extensive heaps of genomic data. The Nucleotide databases like GenBank, size getting 2 to 3 times larger annually. The increase in genomic data outstrips the increase in storage capacity. Massive amount of genomic data needs an effectual depository, quick transposal and preferable performance. To reduce storage of abundant data and data storage expense, compression algorithms were used. Typical compression approaches lose status while compressing these sequences. However, novel compression algorithms have been introduced for better compression ratio. The performance is correlated in terms of compression ratio; ratio of the capacity of compressed file and compression/decompression time; time taken to compress/decompress the sequence. In the proposed work, the input DNA sequence is compressed by reconstructing the sequence into varied formats. Here the input DNA sequence is subjected to bit reduction. The binary output is converted to hexadecimal format followed by encoding. Thus, the compression ratio of the biological sequence is improved.

scholarly journals Scalable Classification of Organisms into a Taxonomy Using Hierarchical Supervised Learners

2020 ◽  
Author(s):  
Gihad N. Sohsah ◽  
Ali Reza Ibrahimzada ◽  
Huzeyfe Ayaz ◽  
Ali Cakmak
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Kernel Method ◽  
Sequence Data ◽  
Dna Barcode ◽  
Living Organism ◽  
Large Datasets ◽  
Living Organisms ◽  
Hierarchical Classifier

Taxonomy of living organisms gains major importance in making the study of vastly heterogeneous living things easier. In addition, various fields of applied biology (e.g., agriculture) depend on classification of living creatures. Specific fragments of the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from three major issues: (i) most of them assume that the classification is done within a given taxonomic class and/or input sequences are prealigned, (ii) highly performing classifiers, such as SVM, cannot scale to large taxonomies due to high memory requirements, (iii) mutations and noise in input DNA sequences greatly reduce the taxonomic classification accuracy. In order to address these issues, we propose a multi-level hierarchical classifier framework to automatically assign taxonomy labels to DNA sequences. We utilize an alignment-free approach called spectrum kernel method for feature extraction. We build a proof-of-concept hierarchical classifier with two levels, and evaluated it on real DNA sequence data from BOLD systems. We demonstrate that the proposed framework provides higher accuracy than regular classifiers. Besides, hierarchical framework scales better to large datasets enabling researchers to employ classifiers with high accuracy and high memory requirement on large datasets. Furthermore, we show that the proposed framework is more robust to mutations and noise in sequence data than the non-hierarchical classifiers.

A New Approach for DNA Sequence Similarity Analysis based on Triplets of Nucleic Acid Bases

2010 ◽  
Vol 2 (4) ◽  
pp. 1-11
Author(s):  
Dan Wei ◽  
Qingshan Jiang ◽  
Sheng Li
Keyword(s):  
Nucleic Acid ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Sequence Similarity ◽  
Similarity Analysis ◽  
Biological Sequence ◽  
Nucleic Acid Bases ◽  
New Approach ◽  
Characteristic Distribution ◽  
Sequence Similarity Analysis

Similarity analysis of DNA sequences is a fundamental research area in Bioinformatics. The characteristic distribution of L-tuple, which is the tuple of length L, reflects the valuable information contained in a biological sequence and thus may be used in DNA sequence similarity analysis. However, similarity analysis based on characteristic distribution of L-tuple is not effective for the comparison of highly conservative sequences. In this paper, a new similarity measurement approach based on Triplets of Nucleic Acid Bases (TNAB) is introduced for DNA sequence similarity analysis. The new approach characterizes both the content feature and position feature of a DNA sequence using the frequency and position of occurrence of TNAB in the sequence. The experimental results show that the approach based on TNAB is effective for analysing DNA sequence similarity.

scholarly journals An Optimal Seed Based Compression Algorithm for DNA Sequences

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Pamela Vinitha Eric ◽  
Gopakumar Gopalakrishnan ◽  
Muralikrishnan Karunakaran
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Lossless Compression ◽  
Compression Algorithm ◽  
Compression Scheme ◽  
Substitution Method ◽  
Compression Algorithms ◽  
Dna Sequence Compression ◽  
Sequence Compression ◽  
Better Than

This paper proposes a seed based lossless compression algorithm to compress a DNA sequence which uses a substitution method that is similar to the LempelZiv compression scheme. The proposed method exploits the repetition structures that are inherent in DNA sequences by creating an offline dictionary which contains all such repeats along with the details of mismatches. By ensuring that only promising mismatches are allowed, the method achieves a compression ratio that is at par or better than the existing lossless DNA sequence compression algorithms.

scholarly journals Estimation of Similarity between DNA Sequences and Its Graphical Representation

2020 ◽  
Vol 9 (1) ◽  
pp. 43-51
Keyword(s):  
Sequence Analysis ◽  
Molecular Biology ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Graphical Representation ◽  
Similarity Analysis ◽  
Biological Sequence ◽  
Field Of Study ◽  
Biological Sequence Analysis ◽  
Wide Range

Bioinformatics, which is now a well known field of study, originated in the context of biological sequence analysis. Recently graphical representation takes place for the research on DNA sequence. Research in biological sequence is mainly based on the function and its structure. Bioinformatics finds wide range of applications specifically in the domain of molecular biology which focuses on the analysis of molecules viz. DNA, RNA, Protein etc. In this review, we mainly deal with the similarity analysis between sequences and graphical representation of DNA sequence.

scholarly journals Compression of text files using genomic code compression algorithm

2018 ◽  
Vol 7 (2.31) ◽  
pp. 69 ◽  
Author(s):  
G Murugesan ◽  
Rosario Gilmary
Keyword(s):  
Data Storage ◽  
Compression Ratio ◽  
Deoxyribonucleic Acid ◽  
Lossless Compression ◽  
Text Compression ◽  
Input File ◽  
File Size ◽  
Compression Algorithms ◽  
Code Compression ◽  
Massive Cluster

Text files utilize substantial amount of memory or disk space. Transmission of these files across a network depends upon a considerable amount of bandwidth. Compression procedures are explicitly advantageous in telecommunications and information technology because it facilitate devices to disseminate or reserve the equivalent amount of data in fewer bits. Text compression techniques section, the English passage by observing the patters and provide alternative symbols for larger patters of text. To diminish the depository of copious information and data storage expenditure, compression algorithms were used. Compression of significant and massive cluster of information can head to the improvement in retrieval time. Novel lossless compression algorithms have been introduced for better compression ratio. In this work, the various existing compression mechanisms that are particular for compressing the text files and Deoxyribonucleic acid (DNA) sequence files are analyzed. The performance is correlated in terms of compression ratio, time taken to compress/decompress the sequence and file size. In this proposed work, the input file is converted to DNA format and then DNA compression procedure is applied.  

scholarly journals Scalable classification of organisms into a taxonomy using hierarchical supervised learners

2020 ◽  
Vol 18 (05) ◽  
pp. 2050026
Author(s):  
Gihad N. Sohsah ◽  
Ali Reza Ibrahimzada ◽  
Huzeyfe Ayaz ◽  
Ali Cakmak
Keyword(s):  
Dna Sequence ◽  
Dna Sequences ◽  
Kernel Method ◽  
Sequence Data ◽  
Genetic Material ◽  
Dna Barcode ◽  
Living Organism ◽  
Large Datasets ◽  
Data Systems ◽  
Hierarchical Classifier

Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from three major issues: (i) most of them assume that the classification is done within a given taxonomic class and/or input sequences are pre-aligned, (ii) highly performing classifiers, such as SVM, cannot scale to large taxonomies due to high memory requirements, (iii) mutations and noise in input DNA sequences greatly reduce the taxonomic classification score. In order to address these issues, we propose a multi-level hierarchical classifier framework to automatically assign taxonomy labels to DNA sequences. We utilize an alignment-free approach called spectrum kernel method for feature extraction. We build a proof-of-concept hierarchical classifier with two levels, and evaluated it on real DNA sequence data from barcode of life data systems. We demonstrate that the proposed framework provides higher f1-score than regular classifiers. Besides, hierarchical framework scales better to large datasets enabling researchers to employ classifiers with high classification performance and high memory requirement on large datasets. Furthermore, we show that the proposed framework is more robust to mutations and noise in sequence data than the non-hierarchical classifiers.

scholarly journals Genomic Sequence Data Compression using Lempel-Ziv-Welch Algorithm with Indexed Multiple Dictionary

2019 ◽  
Vol 9 (2) ◽  
pp. 541-547
Keyword(s):  
Dna Sequence ◽  
High Throughput ◽  
Compression Ratio ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Genomic Data ◽  
General Purpose ◽  
Huge Amount ◽  
Compression Time ◽  
Average Size

With the advancement in technology and development of High Throughput System (HTS), the amount of genomic data generated per day per laboratory across the globe is surpassing the Moore’s law. The huge amount of data generated is of concern to the biologists with respect to their storage as well as transmission across different locations for further analysis. Compression of the genomic data is the wise option to overcome the problems arising from the data deluge. This paper discusses various algorithms that exists for compression of genomic data as well as a few general purpose algorithms and proposes a LZW-based compression algorithm that uses indexed multiple dictionaries for compression. The proposed method exhibits an average compression ratio of 0.41 bits per base and an average compression time of 6.45 secs for a DNA sequence of an average size 105.9 KB.

scholarly journals Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences

2019 ◽  
Vol 35 (19) ◽  
pp. 3826-3828 ◽  
Author(s):  
Kirill Kryukov ◽  
Mahoko Takahashi Ueda ◽  
So Nakagawa ◽  
Tadashi Imanishi
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Compression Ratio ◽  
Dna Sequences ◽  
General Purpose ◽  
Supplementary Information ◽  
File Format ◽  
Storage Space ◽  
Supplementary Data ◽  
Network Transmission

Abstract Summary DNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF)—a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. Nucleotide Archival Format compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli and zstd. Availability and implementation NAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any use. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences

2018 ◽  
Author(s):  
Kirill Kryukov ◽  
Mahoko Takahashi Ueda ◽  
So Nakagawa ◽  
Tadashi Imanishi
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Compression Ratio ◽  
Dna Sequences ◽  
Public Domain ◽  
General Purpose ◽  
Nucleotide Sequences ◽  
File Format ◽  
Storage Space ◽  
Network Transmission

AbstractSummaryDNA sequence databases use compression such as gzip to reduce the required storage space and network transmission time. We describe Nucleotide Archival Format (NAF) – a new file format for lossless reference-free compression of FASTA and FASTQ-formatted nucleotide sequences. NAF compression ratio is comparable to the best DNA compressors, while providing dramatically faster decompression. We compared our format with DNA compressors: DELIMINATE and MFCompress, and with general purpose compressors: gzip, bzip2, xz, brotli, and zstd.AvailabilityNAF compressor and decompressor, as well as format specification are available at https://github.com/KirillKryukov/naf. Format specification is in public domain. Compressor and decompressor are open source under the zlib/libpng license, free for nearly any [email protected]

scholarly journals IMPROVEMENT OF METHODS AND ALGORITHM OF FRACTAL COMPRESSION OF A GRAPHIC IMAGE

2021 ◽  
pp. 118-125
Author(s):  
Olha Zalevskaya ◽  
Petro Yablonskyi ◽  
Iuliia Sydorenko ◽  
Ivan Miroshnichenko ◽  
Akim Sytnik
Keyword(s):  
Data Storage ◽  
Compression Ratio ◽  
Low Frequency ◽  
Main Idea ◽  
Affine Transformations ◽  
Compression Algorithms ◽  
Fractal Compression ◽  
Graphic Data ◽  
Computational Calculations ◽  
Processing Information

During the intensive development of information systems, the volumes of data necessary for storing and processing information, in particular graphic images, grow. One of the reasons for this phenomenon is the desire to continually improve the quality of the content we receive. The ever-growing demand for image quality requires the development of new and improvement of existing approaches to information compression. Compression algorithms use the presence of so-called surplus in the data, which can be eliminated during data storage and restored during playback. Currently in demand methods are based on storing only low-frequency components. Such methods are used in JPEG, MPEG compression algorithms. The disadvantage of such algorithms is not a large compression ratio. In this regard, methods have emerged based on fractal data compression. The main idea of ​​the method is to store the image as affine transformations, which leads to its compression. The paper proposes preliminary processing of graphic data for storing them in the form of a file with the * .json extension. The application of a further fractal compression algorithm to the already obtained file allows to reduce the processing time of data, computational calculations. The resulting file will have the advantages of fractal compression such as decompression speed, better compression ratio and higher resolution compared to * .jpeg and * .bmp. Despite all the advantages, fractal compression of graphic information is used quite rarely. This is due to the complexity of the algorithm, the lack of a sufficient number of specialists on this issue and the estimate of the licensed software. The improvement is aimed at simplifying the algorithm and its implementation will avoid these drawbacks and expand the scope of fractal compression.

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