CALQ: compression of quality values of aligned sequencing data

• Published in: Bioinformatics 2018-05, Vol.34 (10), p.1650-1658
• Main Authors: Voges, Jan, Ostermann, Jörn, Hernaez, Mikel
• Format: Article
• Language: English
• Subjects: Original Papers
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btx737

Abstract Motivation Recent advancements in high-throughput sequencing technology have led to a rapid growth of genomic data. Several lossless compression schemes have been proposed for the coding of such data present in the form of raw FASTQ files and aligned SAM/BAM files. However, due to their high entropy, losslessly compressed quality values account for about 80% of the size of compressed files. For the quality values, we present a novel lossy compression scheme named CALQ. By controlling the coarseness of quality value quantization with a statistical genotyping model, we minimize the impact of the introduced distortion on downstream analyses. Results We analyze the performance of several lossy compressors for quality values in terms of trade-off between the achieved compressed size (in bits per quality value) and the Precision and Recall achieved after running a variant calling pipeline over sequencing data of the well-known NA12878 individual. By compressing and reconstructing quality values with CALQ, we observe a better average variant calling performance than with the original data while achieving a size reduction of about one order of magnitude with respect to the state-of-the-art lossless compressors. Furthermore, we show that CALQ performs as good as or better than the state-of-the-art lossy compressors in terms of variant calling Recall and Precision for most of the analyzed datasets. Availability and implementation CALQ is written in C ++ and can be downloaded from https://github.com/voges/calq. Supplementary information Supplementary data are available at Bioinformatics online.