QVZ: lossy compression of quality values

Malysa, Greg; Hernáez, Mikel; Ochoa, Idoia; Rao, Milind; Ganesan, Karthik; Weissman, Tsachy

doi:10.1093/bioinformatics/btv330

Cited by 57 publications

(33 citation statements)

References 24 publications

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“…To validate the proposed approach the authors compared the variant calling results obtained with lossy compression of QVs using QVZ [4] and Quartz [8] on the dataset with ID 4 listed in Table 1. The golden reference for variant calling was the Illumina Platinum Genomes v8.0.…”

Section: Tools Comparisonmentioning

confidence: 99%

“…The attempt to achieve higher compression rates than those yielded by lossless approaches such as the algorithms employed by SAMtools [3] and other optimized implementations [2] is leading to the study of new lossy schemes for QVs, such as the ones recently appearing in literature [4] [5] [6] [7]. These works point out that in some cases lossy compression of QVs does not negatively affect the quality of analysis results, but seems to actually improve performance of certain analyses such as genotyping (identification of variants with respect to a reference genome) [8].…”

Section: Introductionmentioning

confidence: 99%

“…By these means, coding schemes are compared and ranked at specific bit rates according to the lowest perceived visual or auditory distortion, or for the same perceived distortion, to the lowest bitrate necessary. Although some rate-distortion metrics have been previously proposed [7] [4] for QVs, no consensus around an appropriate definition of distortion exists in the scientific community.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values

Alberti

Daniels

Hernáez

et al. 2016

2016 Data Compression Conference (DCC)

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This paper provides the specification and an initial validation of an evaluation framework for the comparison of lossy compressors of genome sequencing quality values. The goal is to define reference data, test sets, tools and metrics that shall be used to evaluate the impact of lossy compression of quality values on human genome variant calling. The functionality of the framework is validated referring to two state-of-the-art genomic compressors. This work has been spurred by the current activity within the ISO/IEC SC29/WG11 technical committee (a.k.a. MPEG), which is investigating the possibility of starting a standardization activity for genomic information representation.

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Section: Tools Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Evaluation Framework for Lossy Compression of Genome Sequencing Quality Values

Alberti

Daniels

Hernáez

et al. 2016

2016 Data Compression Conference (DCC)

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“…We compare the performance of the proposed algorithm CROMqs with that of the state-of-the-art lossy compressors for quality scores. In particular, we consider the lossy compressors Rblock and Pblock, presented in [7], as well as QVZ [8]. …”

Section: Resultsmentioning

confidence: 99%

“…Moreover, there is evidence that quality scores are corrupted by some amount of noise introduced during sequencing, mainly due to the use of inaccurate models to estimate the probabilities of error [5]. Thus, whereas lossless compression is preferred for the reads, lossy compression of quality scores has emerged as a natural candidate to boost compression performance (see [6]–[8] and references therein).…”

Section: Introductionmentioning

confidence: 99%

CROMqs: an infinitesimal successive refinement lossy compressor for the quality scores

Ochoa

Hernáez

et al. 2016

2016 IEEE Information Theory Workshop (ITW)

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Massive amounts of sequencing data are being generated thanks to advances in sequencing technology and a dramatic drop in the sequencing cost. Much of the data are comprised of nucleotides and the corresponding quality scores that indicate their reliability. The latter are more difficult to compress and are themselves noisy. As a result, lossy compression of the quality scores has recently been proposed to alleviate the storage costs. Further, it has been shown that lossy compression, at some specific rates, can achieve a performance on variant calling similar to that achieved with the lossless compressed data. We propose CROMqs, a new lossy compressor for the quality scores with the property of “infinitesimal successive refinability”. This property allows the decoder to decompress the data iteratively without the need of agreeing with the encoder on a specific rate prior to compression. This characteristic is particularly amenable in practice, as in most cases the appropriate rate at which the lossy compressor should operate can not be established prior to compression. Further, this property can be of interest in scenarios involving streaming of genomic data. CROMqs is the first infinitesimal successive refinement lossy compressor for the quality scores in the literature, and we show that it obtains a comparable rate-distortion performance to previously proposed algorithms. Moreover, we also show that CROMqs achieves a comparable performance on variant calling to that of the lossless compressed data.

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