Limits...
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.


Conceptual model of FRESCO’s execution.(C1) Reference genome indexing. (C2) Compression of the input genome(s), using the indexed reference. (C3) Encoding of the preliminary results to produce the final file.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4493149&req=5

pone.0132460.g001: Conceptual model of FRESCO’s execution.(C1) Reference genome indexing. (C2) Compression of the input genome(s), using the indexed reference. (C3) Encoding of the preliminary results to produce the final file.

Mentions: From this point on, we use only FRESCO as the baseline to our study since it already was compared to and outperforms all other related tools in terms of execution time, and achieves competitive compression ratios [15]. FRESCO is a lossless referential compression library for DNA sequences, stored in RAW or FASTA formats. It is written in C++ and is open source [15]. Compressing a genome in FRESCO requires three steps (see Fig 1): (C1) indexing, (C2) compression itself and (C3) encoding.


On-Demand Indexing for Referential Compression of DNA Sequences.

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

Conceptual model of FRESCO’s execution.(C1) Reference genome indexing. (C2) Compression of the input genome(s), using the indexed reference. (C3) Encoding of the preliminary results to produce the final file.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132460.g001: Conceptual model of FRESCO’s execution.(C1) Reference genome indexing. (C2) Compression of the input genome(s), using the indexed reference. (C3) Encoding of the preliminary results to produce the final file.
Mentions: From this point on, we use only FRESCO as the baseline to our study since it already was compared to and outperforms all other related tools in terms of execution time, and achieves competitive compression ratios [15]. FRESCO is a lossless referential compression library for DNA sequences, stored in RAW or FASTA formats. It is written in C++ and is open source [15]. Compressing a genome in FRESCO requires three steps (see Fig 1): (C1) indexing, (C2) compression itself and (C3) encoding.

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.