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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.


Index lookup.If the search window δ does not contain a match, then we index Δ base pairs and execute one table lookup.
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pone.0132460.g005: Index lookup.If the search window δ does not contain a match, then we index Δ base pairs and execute one table lookup.

Mentions: Our last method, if all others failed to find a match, indexes a small sequence block from the reference and performs a table lookup. It resembles FRESCO’s algorithm, but it populates the K-mer table with only Δ base pairs instead of the entire reference sequence. Fig 5 shows the point a brute-force search fails, which is the time we start populating the index table and perform a lookup on it. The lookup uses a segment of size K from the to-be-compressed sequence. The only difference, compared to FRESCO’s algorithm, is that we select as the best match the value which is closer to RP.


On-Demand Indexing for Referential Compression of DNA Sequences.

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

Index lookup.If the search window δ does not contain a match, then we index Δ base pairs and execute one table lookup.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132460.g005: Index lookup.If the search window δ does not contain a match, then we index Δ base pairs and execute one table lookup.
Mentions: Our last method, if all others failed to find a match, indexes a small sequence block from the reference and performs a table lookup. It resembles FRESCO’s algorithm, but it populates the K-mer table with only Δ base pairs instead of the entire reference sequence. Fig 5 shows the point a brute-force search fails, which is the time we start populating the index table and perform a lookup on it. The lookup uses a segment of size K from the to-be-compressed sequence. The only difference, compared to FRESCO’s algorithm, is that we select as the best match the value which is closer to RP.

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.