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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.


RME graph for the sequences from Example 1.The graph has 8 nodes and 8 edges. The edge from (1,0,8,C) to (0,10,13,T) occurs two times: one time for the factorization of s1 and another time for the factorization of s2.
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pone.0139000.g002: RME graph for the sequences from Example 1.The graph has 8 nodes and 8 edges. The edge from (1,0,8,C) to (0,10,13,T) occurs two times: one time for the factorization of s1 and another time for the factorization of s2.

Mentions: Example 3The RME graph for the sequences from Example 1 is shown in Fig 2.


Sequence Factorization with Multiple References.

Wandelt S, Leser U - PLoS ONE (2015)

RME graph for the sequences from Example 1.The graph has 8 nodes and 8 edges. The edge from (1,0,8,C) to (0,10,13,T) occurs two times: one time for the factorization of s1 and another time for the factorization of s2.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139000.g002: RME graph for the sequences from Example 1.The graph has 8 nodes and 8 edges. The edge from (1,0,8,C) to (0,10,13,T) occurs two times: one time for the factorization of s1 and another time for the factorization of s2.
Mentions: Example 3The RME graph for the sequences from Example 1 is shown in Fig 2.

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.