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On-Demand Indexing for Referential Compression of DNA Sequences.

Alves F, Cogo V, Wandelt S, Leser U, Bessani A - PLoS ONE (2015)

Bottom Line: Referential compression is one of these techniques, in which the similarity between the DNA of organisms of the same or an evolutionary close species is exploited to reduce the storage demands of genome sequences up to 700 times.The general idea is to store in the compressed file only the differences between the to-be-compressed and a well-known reference sequence.Our approach, called On-Demand Indexing (ODI) compresses human chromosomes five to ten times faster than other state-of-the-art tools (on average), while achieving similar compression ratios.

View Article: PubMed Central - PubMed

Affiliation: LaSIGE, University of Lisbon, Lisbon, Portugal.

ABSTRACT
The decreasing costs of genome sequencing is creating a demand for scalable storage and processing tools and techniques to deal with the large amounts of generated data. Referential compression is one of these techniques, in which the similarity between the DNA of organisms of the same or an evolutionary close species is exploited to reduce the storage demands of genome sequences up to 700 times. The general idea is to store in the compressed file only the differences between the to-be-compressed and a well-known reference sequence. In this paper, we propose a method for improving the performance of referential compression by removing the most costly phase of the process, the complete reference indexing. Our approach, called On-Demand Indexing (ODI) compresses human chromosomes five to ten times faster than other state-of-the-art tools (on average), while achieving similar compression ratios.

No MeSH data available.


Brute force search.We test every combination of K base pairs within the δ window for a match.
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pone.0132460.g004: Brute force search.We test every combination of K base pairs within the δ window for a match.

Mentions: This test performs a brute-force search within a window of δ base pairs when the first two steps failed to find a match. It first tests the to-be-compressed segment of size K starting in the position CP against all reference segments of size K within the δ window. Fig 4 shows this matching approach. It covers mutations of up to δ − K base pairs. The value of δ is configurable and should be adjusted to the species of the input sequences.


On-Demand Indexing for Referential Compression of DNA Sequences.

Alves F, Cogo V, Wandelt S, Leser U, Bessani A - PLoS ONE (2015)

Brute force search.We test every combination of K base pairs within the δ window for a match.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493149&req=5

pone.0132460.g004: Brute force search.We test every combination of K base pairs within the δ window for a match.
Mentions: This test performs a brute-force search within a window of δ base pairs when the first two steps failed to find a match. It first tests the to-be-compressed segment of size K starting in the position CP against all reference segments of size K within the δ window. Fig 4 shows this matching approach. It covers mutations of up to δ − K base pairs. The value of δ is configurable and should be adjusted to the species of the input sequences.

Bottom Line: Referential compression is one of these techniques, in which the similarity between the DNA of organisms of the same or an evolutionary close species is exploited to reduce the storage demands of genome sequences up to 700 times.The general idea is to store in the compressed file only the differences between the to-be-compressed and a well-known reference sequence.Our approach, called On-Demand Indexing (ODI) compresses human chromosomes five to ten times faster than other state-of-the-art tools (on average), while achieving similar compression ratios.

View Article: PubMed Central - PubMed

Affiliation: LaSIGE, University of Lisbon, Lisbon, Portugal.

ABSTRACT
The decreasing costs of genome sequencing is creating a demand for scalable storage and processing tools and techniques to deal with the large amounts of generated data. Referential compression is one of these techniques, in which the similarity between the DNA of organisms of the same or an evolutionary close species is exploited to reduce the storage demands of genome sequences up to 700 times. The general idea is to store in the compressed file only the differences between the to-be-compressed and a well-known reference sequence. In this paper, we propose a method for improving the performance of referential compression by removing the most costly phase of the process, the complete reference indexing. Our approach, called On-Demand Indexing (ODI) compresses human chromosomes five to ten times faster than other state-of-the-art tools (on average), while achieving similar compression ratios.

No MeSH data available.