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Sequence Factorization with Multiple References.

Wandelt S, Leser U - PLoS ONE (2015)

Bottom Line: Our results show a wide range of factorization sizes (optimal to an overhead of up to 300%), factorization speed (0.01 MB/s to more than 600 MB/s), and main memory usage (few dozen MB to dozens of GB).Based on our evaluation, we identify the best configurations for common use cases.Our evaluation shows that multi-reference factorization is much better than single-reference factorization.

View Article: PubMed Central - PubMed

Affiliation: Knowledge Management in Bioinformatics, Humboldt-University of Berlin, Rudower Chaussee 25, 12489 Berlin, Germany.

ABSTRACT
The success of high-throughput sequencing has lead to an increasing number of projects which sequence large populations of a species. Storage and analysis of sequence data is a key challenge in these projects, because of the sheer size of the datasets. Compression is one simple technology to deal with this challenge. Referential factorization and compression schemes, which store only the differences between input sequence and a reference sequence, gained lots of interest in this field. Highly-similar sequences, e.g., Human genomes, can be compressed with a compression ratio of 1,000:1 and more, up to two orders of magnitude better than with standard compression techniques. Recently, it was shown that the compression against multiple references from the same species can boost the compression ratio up to 4,000:1. However, a detailed analysis of using multiple references is lacking, e.g., for main memory consumption and optimality. In this paper, we describe one key technique for the referential compression against multiple references: The factorization of sequences. Based on the notion of an optimal factorization, we propose optimization heuristics and identify parameter settings which greatly influence 1) the size of the factorization, 2) the time for factorization, and 3) the required amount of main memory. We evaluate a total of 30 setups with a varying number of references on data from three different species. Our results show a wide range of factorization sizes (optimal to an overhead of up to 300%), factorization speed (0.01 MB/s to more than 600 MB/s), and main memory usage (few dozen MB to dozens of GB). Based on our evaluation, we identify the best configurations for common use cases. Our evaluation shows that multi-reference factorization is much better than single-reference factorization.

No MeSH data available.


Factorization speed of optimal factorization techniques: based on ESA (top) and based on CST (bottom).The factorization speed significantly, but non-linearly reduces with an increasing number of references. Factorization with ESA index is more than two orders of magnitude faster than CST-based techniques.
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pone.0139000.g005: Factorization speed of optimal factorization techniques: based on ESA (top) and based on CST (bottom).The factorization speed significantly, but non-linearly reduces with an increasing number of references. Factorization with ESA index is more than two orders of magnitude faster than CST-based techniques.

Mentions: We analyzed the factorization speed of selected configurations. First, we evaluated the factorization speed of optimal factorization techniques. The result for AT1, HG21, and yeast is shown in Fig 5. We do not show the results for AT5, HG1, and HG10, since factorization speed within a species is quite stable. The fastest factorization is obtained for HG21 (up to 170MB/s), since a single lookup often finds a long match in the reference sequence(s). Factorizing AT1 is already slower by a factor of four and yeast again two times slower.


Sequence Factorization with Multiple References.

Wandelt S, Leser U - PLoS ONE (2015)

Factorization speed of optimal factorization techniques: based on ESA (top) and based on CST (bottom).The factorization speed significantly, but non-linearly reduces with an increasing number of references. Factorization with ESA index is more than two orders of magnitude faster than CST-based techniques.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589410&req=5

pone.0139000.g005: Factorization speed of optimal factorization techniques: based on ESA (top) and based on CST (bottom).The factorization speed significantly, but non-linearly reduces with an increasing number of references. Factorization with ESA index is more than two orders of magnitude faster than CST-based techniques.
Mentions: We analyzed the factorization speed of selected configurations. First, we evaluated the factorization speed of optimal factorization techniques. The result for AT1, HG21, and yeast is shown in Fig 5. We do not show the results for AT5, HG1, and HG10, since factorization speed within a species is quite stable. The fastest factorization is obtained for HG21 (up to 170MB/s), since a single lookup often finds a long match in the reference sequence(s). Factorizing AT1 is already slower by a factor of four and yeast again two times slower.

Bottom Line: Our results show a wide range of factorization sizes (optimal to an overhead of up to 300%), factorization speed (0.01 MB/s to more than 600 MB/s), and main memory usage (few dozen MB to dozens of GB).Based on our evaluation, we identify the best configurations for common use cases.Our evaluation shows that multi-reference factorization is much better than single-reference factorization.

View Article: PubMed Central - PubMed

Affiliation: Knowledge Management in Bioinformatics, Humboldt-University of Berlin, Rudower Chaussee 25, 12489 Berlin, Germany.

ABSTRACT
The success of high-throughput sequencing has lead to an increasing number of projects which sequence large populations of a species. Storage and analysis of sequence data is a key challenge in these projects, because of the sheer size of the datasets. Compression is one simple technology to deal with this challenge. Referential factorization and compression schemes, which store only the differences between input sequence and a reference sequence, gained lots of interest in this field. Highly-similar sequences, e.g., Human genomes, can be compressed with a compression ratio of 1,000:1 and more, up to two orders of magnitude better than with standard compression techniques. Recently, it was shown that the compression against multiple references from the same species can boost the compression ratio up to 4,000:1. However, a detailed analysis of using multiple references is lacking, e.g., for main memory consumption and optimality. In this paper, we describe one key technique for the referential compression against multiple references: The factorization of sequences. Based on the notion of an optimal factorization, we propose optimization heuristics and identify parameter settings which greatly influence 1) the size of the factorization, 2) the time for factorization, and 3) the required amount of main memory. We evaluate a total of 30 setups with a varying number of references on data from three different species. Our results show a wide range of factorization sizes (optimal to an overhead of up to 300%), factorization speed (0.01 MB/s to more than 600 MB/s), and main memory usage (few dozen MB to dozens of GB). Based on our evaluation, we identify the best configurations for common use cases. Our evaluation shows that multi-reference factorization is much better than single-reference factorization.

No MeSH data available.