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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 selected approximate configurations with one reference (left) and ten references (right).More references do not degrade factorization speed as much as in optimal factorization. Local matching on a KMER-index provides the highest factorization speeds for all datasets.
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pone.0139000.g006: Factorization speed of selected approximate configurations with one reference (left) and ten references (right).More references do not degrade factorization speed as much as in optimal factorization. Local matching on a KMER-index provides the highest factorization speeds for all datasets.

Mentions: Factorization speed slowly decreases with an increasing number of references, since for each suffix, more lookups have to be performed. At the same time, the average match length increases (see our results in Fig 3), which means that less lookups need to be performed overall. This increase in match length causes non-linear curves for factorization speed in Fig 6: Optimal factorization against 10 references is on average only four times slower than optimal compression against a single reference.


Sequence Factorization with Multiple References.

Wandelt S, Leser U - PLoS ONE (2015)

Factorization speed of selected approximate configurations with one reference (left) and ten references (right).More references do not degrade factorization speed as much as in optimal factorization. Local matching on a KMER-index provides the highest factorization speeds for all datasets.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139000.g006: Factorization speed of selected approximate configurations with one reference (left) and ten references (right).More references do not degrade factorization speed as much as in optimal factorization. Local matching on a KMER-index provides the highest factorization speeds for all datasets.
Mentions: Factorization speed slowly decreases with an increasing number of references, since for each suffix, more lookups have to be performed. At the same time, the average match length increases (see our results in Fig 3), which means that less lookups need to be performed overall. This increase in match length causes non-linear curves for factorization speed in Fig 6: Optimal factorization against 10 references is on average only four times slower than optimal compression against a single reference.

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.