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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.


Average occurrences count of k-mer instances with varying k.For all six datasets independently, a value of k = 16 guaranteed that each 16-mer had an average number of occurrences smaller than 2 in the sequences.
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pone.0139000.g010: Average occurrences count of k-mer instances with varying k.For all six datasets independently, a value of k = 16 guaranteed that each 16-mer had an average number of occurrences smaller than 2 in the sequences.

Mentions: We evaluated parameters for indexing and match finding techniques first. The choice of k for a k-mer index (k-mer indexes are used for referential compression as described in [18]) has a significant impact on factorization speed and factorization rate: too small k will lead to high verification costs, since a k-mer can occur very frequently in the references and too large k will increase the number of missed matches. In Fig 10, we show the average number of occurrences of all k-mers for our datasets depending on k. We have analyzed a range of 10 ≤ k ≤ 20. If we choose k ≥ 16, most k-mers are unique (note that the curve is growing exponentially to the left; see for instance HG1). We decided to set k = 16: then the average number of occurrences for k-mers is less than 2 for each dataset.


Sequence Factorization with Multiple References.

Wandelt S, Leser U - PLoS ONE (2015)

Average occurrences count of k-mer instances with varying k.For all six datasets independently, a value of k = 16 guaranteed that each 16-mer had an average number of occurrences smaller than 2 in the sequences.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139000.g010: Average occurrences count of k-mer instances with varying k.For all six datasets independently, a value of k = 16 guaranteed that each 16-mer had an average number of occurrences smaller than 2 in the sequences.
Mentions: We evaluated parameters for indexing and match finding techniques first. The choice of k for a k-mer index (k-mer indexes are used for referential compression as described in [18]) has a significant impact on factorization speed and factorization rate: too small k will lead to high verification costs, since a k-mer can occur very frequently in the references and too large k will increase the number of missed matches. In Fig 10, we show the average number of occurrences of all k-mers for our datasets depending on k. We have analyzed a range of 10 ≤ k ≤ 20. If we choose k ≥ 16, most k-mers are unique (note that the curve is growing exponentially to the left; see for instance HG1). We decided to set k = 16: then the average number of occurrences for k-mers is less than 2 for each dataset.

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.