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QVZ: lossy compression of quality values
Greg Malysa1, Mikel Hernaez1, Idoia Ochoa1
1Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Bioinformatics (Oxford, England)
|May 31, 2015
Summary
A new lossy compressor, QVZ, efficiently compresses genomic data quality values, reducing storage needs. QVZ outperforms existing methods and improves genotyping accuracy, making genomic data management more effective.
Area of Science:
- Genomics
- Bioinformatics
- Data Compression
Background:
- Advancements in sequencing technology have led to a significant increase in genomic data generation.
- Genomic data files (FASTQ, SAM) require substantial storage, processing, and transmission capabilities.
- Quality values constitute approximately half of the uncompressed storage space in genomic data.
Purpose of the Study:
- To propose a novel lossy compressor for genomic data quality values.
- To reduce the storage requirements of large-scale genomic datasets.
- To improve the efficiency of genomic data processing and transmission.
Main Methods:
- Development of a new lossy compression algorithm named QVZ.
- Evaluation of QVZ's rate-distortion performance against existing algorithms.
- Assessment of QVZ's impact on genotyping accuracy.
Main Results:
- QVZ demonstrates superior rate-distortion performance compared to previous algorithms across various distortion metrics.
- QVZ supports user-defined quasi-convex distortion functions, a novel capability.
- QVZ-compressed data yield improved genotyping accuracy at similar compression rates.
Conclusions:
- QVZ offers an effective solution for compressing genomic quality values, addressing the challenges of big genomic data.
- The algorithm provides flexibility in distortion function minimization.
- QVZ enhances downstream genomic analyses, such as genotyping, by preserving data integrity.
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