A Compression Model for DNA Multiple Sequence Alignment Blocks

Matos, Luís M. O.; Pratas, Diogo; Pinho, Armando J.

doi:10.1109/tit.2012.2236605

Cited by 13 publications

(14 citation statements)

References 27 publications

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“…Recently, we proposed a compression method based on a mixture of finite-context models and arithmetic coding, for compressing the MSABs [ 11 , 28 ]. This compressor algorithm was designed to compress only the DNA bases and alignment gaps of the ‘s’ lines, without considering other information, nor the possible presence of lower case DNA bases.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we have proposed an algorithm, based on a mixture of finite-context models and arithmetic coding, for compressing only the MSABs (Multiple Sequence Alignment Blocks) of the MAF [ 16 ] files [ 11 ]. This algorithm was only designed to compress the DNA bases and alignment gaps that are present in the MSABs.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, non-ACGT letters (including lower case) were also not considered. Therefore, the approach described in [ 11 ] is lossy in the sense that it does not permit the full recovery of the original file. The MAFCO tool described in this paper is a fully lossless compression method, capable of handling all data found in a MAF file.…”

Section: Introductionmentioning

confidence: 99%

“…In 2013, we proposed a compression algorithm for the ‘s’ lines of the MSABs [ 11 ], based on a mixture of finite-context models and arithmetic coding. It is not a full compression tool, because it ignores all the optional lines and the MSAB header information (source name, source size, etc.).…”

Section: Introductionmentioning

confidence: 99%

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MAFCO: A Compression Tool for MAF Files

et al. 2015

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In the last decade, the cost of genomic sequencing has been decreasing so much that researchers all over the world accumulate huge amounts of data for present and future use. These genomic data need to be efficiently stored, because storage cost is not decreasing as fast as the cost of sequencing. In order to overcome this problem, the most popular general-purpose compression tool, gzip, is usually used. However, these tools were not specifically designed to compress this kind of data, and often fall short when the intention is to reduce the data size as much as possible. There are several compression algorithms available, even for genomic data, but very few have been designed to deal with Whole Genome Alignments, containing alignments between entire genomes of several species. In this paper, we present a lossless compression tool, MAFCO, specifically designed to compress MAF (Multiple Alignment Format) files. Compared to gzip, the proposed tool attains a compression gain from 34% to 57%, depending on the data set. When compared to a recent dedicated method, which is not compatible with some data sets, the compression gain of MAFCO is about 9%. Both source-code and binaries for several operating systems are freely available for non-commercial use at: http://bioinformatics.ua.pt/software/mafco.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MAFCO: A Compression Tool for MAF Files

et al. 2015

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“…Some algorithms have been proposed for their lossy and lossless compression [79][80][81]. Moreover, the Variant Call Format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations [82].…”

Section: File Formatsmentioning

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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Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies

Giancarlo¹,

Rombo²,

Utro³

2013

Briefings in Bioinformatics

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High-throughput sequencing technologies produce large collections of data, mainly DNA sequences with additional information, requiring the design of efficient and effective methodologies for both their compression and storage. In this context, we first provide a classification of the main techniques that have been proposed, according to three specific research directions that have emerged from the literature and, for each, we provide an overview of the current techniques. Finally, to make this review useful to researchers and technicians applying the existing software and tools, we include a synopsis of the main characteristics of the described approaches, including details on their implementation and availability. Performance of the various methods is also highlighted, although the state of the art does not lend itself to a consistent and coherent comparison among all the methods presented here.

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A Compression Model for DNA Multiple Sequence Alignment Blocks

Cited by 13 publications

References 27 publications

MAFCO: A Compression Tool for MAF Files

MAFCO: A Compression Tool for MAF Files

A Survey on Data Compression Methods for Biological Sequences

Compressive biological sequence analysis and archival in the era of high-throughput sequencing technologies

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