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QVZ: lossy compression of quality values

Authors: Greg Malysa; Mikel Hernaez; Idoia Ochoa; Milind Rao; Karthik Ganesan 0001; Tsachy Weissman;

QVZ: lossy compression of quality values

Abstract

Abstract Motivation Recent advancements in sequencing technology have led to a drastic reduction in the cost of sequencing a genome. This has generated an unprecedented amount of genomic data that must be stored, processed and transmitted. To facilitate this effort, we propose a new lossy compressor for the quality values presented in genomic data files (e.g. FASTQ and SAM files), which comprise roughly half of the storage space (in the uncompressed domain). Lossy compression allows for compression of data beyond its lossless limit. Results The proposed algorithm QVZ exhibits better rate-distortion performance than the previously proposed algorithms, for several distortion metrics and for the lossless case. Moreover, it allows the user to define any quasi-convex distortion function to be minimized, a feature not supported by the previous algorithms. Finally, we show that QVZ-compressed data exhibit better performance in the genotyping than data compressed with previously proposed algorithms, in the sense that for a similar rate, a genotyping closer to that achieved with the original quality values is obtained. Availability and implementation QVZ is written in C and can be downloaded from https://github.com/mikelhernaez/qvz. Contact mhernaez@stanford.edu or gmalysa@stanford.edu or iochoa@stanford.edu Supplementary information Supplementary data are available at Bioinformatics online.

Country
Spain
Related Organizations
Keywords

Genotype, Genotyping Techniques, Quality values, QVZ, Data Compression, Polymorphism, Single Nucleotide, Databases, Genetic, Lossy compression, Animals, Humans, Algorithms

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
52
Top 10%
Top 10%
Top 10%
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