GeCo2: An Optimized Tool for Lossless Compression and Analysis of DNA Sequences

Pratas, Diogo; Hosseini, Morteza; Pinho, Armando J.

doi:10.1007/978-3-030-23873-5_17

Cited by 17 publications

(25 citation statements)

References 26 publications

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“…e DNA sequence corpus contains 17 DNA sequences with different sizes and reflects the main domains and kingdoms. HRCM selected BuEb sequence which is a DNA sequence corpus as the reference sequence to compress others and compared the results with widely used general-purpose compression methods gzip, bzip2, lzma, and ppmd and the state-of-the-art special purpose genome compression methods MFCompress [32] and GeCo2 [34]. MFCompress tool was tested in default mode and the best mode (denoted by MFC-2 and MFC-3, respectively, according to the original paper).…”

Section: Compression Results and Analysis Of Only One-versionmentioning

confidence: 99%

“…DELIMINATE achieves better compression ratio than general-purpose compression methods although it uses almost the same compression time. In 2011, 2013, 2016, and 2019, respectively, Pinho et al published DNAEnc3 [31], MFCompress [32], GeCo [33], and GeCo2 [34] based on Markov models. DNAEnc3 partitions sequence to nonoverlapping blocks of fixed size, which are then encoded by the best one of the Markov models of different orders.…”

Section: Related Workmentioning

confidence: 99%

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HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

Yao

et al. 2019

BioMed Research International

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With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

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Section: Compression Results and Analysis Of Only One-versionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

Yao

et al. 2019

BioMed Research International

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“…There are also algorithms to cope with low computational resources. From our experience, we would highlight XM [ 68 ] and GeCo/GeCo2 [ 86 , 88 ] given their ability to compress genomic sequences with high compression ratios. On average, XM is slightly better concerning compression ratio (approximately 0.4% and 0.2% over GeCo and GeCo2, respectively).…”

Section: Resultsmentioning

confidence: 99%

“…It has sub-programs to deal with inverted repeats and uses cache-hashes for deeper context models. In GeCo2 [ 88 ], the mixture of models is enhanced, where each context model or tolerant context model now has a specific decay factor. Additionally, specific cache-hash sizes and the ability to run only a context model with inverted repeats are available.…”

Section: Introductionmentioning

confidence: 99%

A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models

Pratas

Hosseini

Silva

et al. 2019

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The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches, on a balanced and diverse benchmark, using a competitive level of computational resources. An efficient implementation of the method is publicly available, under the GPLv3 license.

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“…Figure 1 depicts the complexity profiles of four human Herpesvirus whole genomes using the same scale, where redundant regions are highlighted with blue (below a Bps of one). GTO uses GeCo2 [6] and AC [7] compressors to estimate the local complexity of DNA and amino acid sequences, respectively. However, GTO is not limited to use these data compressors.…”

Section: Validationmentioning

confidence: 99%

GTO: a toolkit to unify pipelines in genomic and proteomic research

Almeida

Pinho

Oliveira

et al. 2020

Preprint

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Next-generation sequencing triggered the production of a massive volume of publicly available data and the development of new specialised tools. These tools are dispersed over different frameworks, making the management and analyses of the data a challenging task. Additionally, new targeted tools are needed, given the dynamics and specificities of the field. We present GTO, a comprehensive toolkit designed to unify pipelines in genomic and proteomic research, which combines specialised tools for analysis, simulation, compression, development, visualisation, and transformation of the data. This toolkit combines novel tools with a modular architecture, being an excellent platform for experimental scientists, as well as a useful resource for teaching bioinformatics inquiry to students in life sciences.

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GeCo2: An Optimized Tool for Lossless Compression and Analysis of DNA Sequences

Cited by 17 publications

References 26 publications

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models

GTO: a toolkit to unify pipelines in genomic and proteomic research

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