Bacteria DNA sequence compression using a mixture of finite-context models

Pinho, Armando J.; Pratas, Diogo; Ferreira, Paulo J. S. G.

doi:10.1109/ssp.2011.5967637

Cited by 38 publications

(27 citation statements)

References 11 publications

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“…The GeCo algorithm [50], derived from [51,52], exploits a combination of context models of several orders for reference-free, as well as for reference-based genomic sequence compression. In this method, extended finite-context models (XFCMs), that are tolerant against substitution errors, are introduced.…”

Section: Reference-free Methodsmentioning

confidence: 99%

A Survey on Data Compression Methods for Biological Sequences

2016

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Abstract:The ever increasing growth of the production of high-throughput sequencing data poses a serious challenge to the storage, processing and transmission of these data. As frequently stated, it is a data deluge. Compression is essential to address this challenge-it reduces storage space and processing costs, along with speeding up data transmission. In this paper, we provide a comprehensive survey of existing compression approaches, that are specialized for biological data, including protein and DNA sequences. Also, we devote an important part of the paper to the approaches proposed for the compression of different file formats, such as FASTA, as well as FASTQ and SAM/BAM, which contain quality scores and metadata, in addition to the biological sequences. Then, we present a comparison of the performance of several methods, in terms of compression ratio, memory usage and compression/decompression time. Finally, we present some suggestions for future research on biological data compression.

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Section: Reference-free Methodsmentioning

confidence: 99%

A Survey on Data Compression Methods for Biological Sequences

2016

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“…Working in DNA compression was initially presented by Grumbach and Tahi in their pioneer work of DNA sequences compression by BioCompress Algorithm (Pinho et al, 2011) and its second version BioCompress-2, these algorithms are based on Ziv-Lempel compression technique (Berger and Mortensen, 2010), BioCompress-2 search for exact repeats in already encoded sequences, then encodes that repeats by repeat length and the position of preceding repeat appeared, when no repetition is found it uses order-2 arithmetic coding (Lin et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

A Deoxyribonucleic Acid Compression Algorithm Using Auto-Regression and Swarm Intelligence

Aly¹,

Yousif²,

Zohdy³

2013

Journal of Computer Science

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DNA compression challenge has become a major task for many researchers as a result of exponential increase of produced DNA sequences in gene databases; in this research we attempt to solve the DNA compression challenge by developing a lossless compression algorithm. The proposed algorithm works in horizontal mode using a substitutional-statistical technique which is based on Auto Regression modeling (AR), the model parameters are determined using Particle Swarm Optimization (PSO). This algorithm is called Swarm Auto-Regression DNA Compression (SARDNAComp). SARDNAComp aims to reach higher compression ratio which make its application beneficial for both practical and functional aspects due to reduction of storage, retrieval, transmission costs and inferring structure and function of sequences from compression, SARDNAComp is tested on eleven benchmark DNA sequences and compared to current algorithms of DNA compression, the results showed that (SARDNAComp) outperform these algorithms.

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“…Instead, they start with a uniformly distributed model, and update it continuously, for example, through the use of counters, to estimate the probabilistic models based on incoming symbols [15].…”

Section: Updating Probabilistic Modelsmentioning

confidence: 99%

“…Finally, FCM gained protagonism in [13][14][15][16], implemented up to order-16. As in the previous cases, the FCMs were followed by an arithmetic encoder, and several different-order FCMs compete.…”

Section: Introductionmentioning

confidence: 99%

Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

Duarte

Pinho

2013

Entropy

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It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs) have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 × 10 9 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN) to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model) and FCM-Mx (mixture of finite-context models) , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

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Bacteria DNA sequence compression using a mixture of finite-context models

Cited by 38 publications

References 11 publications

A Survey on Data Compression Methods for Biological Sequences

A Survey on Data Compression Methods for Biological Sequences

A Deoxyribonucleic Acid Compression Algorithm Using Auto-Regression and Swarm Intelligence

Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

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