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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.


Time spent in the execution of the entire compression workflow.It considers the duration each tool takes to load, index, compress, and write the compressed result to file.
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pone.0132460.g007: Time spent in the execution of the entire compression workflow.It considers the duration each tool takes to load, index, compress, and write the compressed result to file.

Mentions: The complete execution of the compression workflow includes the startup time, file reading, memory allocation, reference indexing, compression, encoding the intermediate result and file writing. The resulting values can be seen in Fig 7.


On-Demand Indexing for Referential Compression of DNA Sequences.

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

Time spent in the execution of the entire compression workflow.It considers the duration each tool takes to load, index, compress, and write the compressed result to file.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132460.g007: Time spent in the execution of the entire compression workflow.It considers the duration each tool takes to load, index, compress, and write the compressed result to file.
Mentions: The complete execution of the compression workflow includes the startup time, file reading, memory allocation, reference indexing, compression, encoding the intermediate result and file writing. The resulting values can be seen in Fig 7.

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.