Dataset Information

NGC: lossless and lossy compression of aligned high-throughput sequencing data.

ABSTRACT: A major challenge of current high-throughput sequencing experiments is not only the generation of the sequencing data itself but also their processing, storage and transmission. The enormous size of these data motivates the development of data compression algorithms usable for the implementation of the various storage policies that are applied to the produced intermediate and final result files. In this article, we present NGC, a tool for the compression of mapped short read data stored in the wide-spread SAM format. NGC enables lossless and lossy compression and introduces the following two novel ideas: first, we present a way to reduce the number of required code words by exploiting common features of reads mapped to the same genomic positions; second, we present a highly configurable way for the quantization of per-base quality values, which takes their influence on downstream analyses into account. NGC, evaluated with several real-world data sets, saves 33-66% of disc space using lossless and up to 98% disc space using lossy compression. By applying two popular variant and genotype prediction tools to the decompressed data, we could show that the lossy compression modes preserve >99% of all called variants while outperforming comparable methods in some configurations.

SUBMITTER: Popitsch N

PROVIDER: S-EPMC3592443 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Publications

NGC: lossless and lossy compression of aligned high-throughput sequencing data.

Popitsch Niko N von Haeseler Arndt A

Nucleic acids research 20121012 1

A major challenge of current high-throughput sequencing experiments is not only the generation of the sequencing data itself but also their processing, storage and transmission. The enormous size of these data motivates the development of data compression algorithms usable for the implementation of the various storage policies that are applied to the produced intermediate and final result files. In this article, we present NGC, a tool for the compression of mapped short read data stored in the w ...[more]

PMID: 23066097

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Project description:BACKGROUND:Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. RESULTS:In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. CONCLUSIONS:Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results.

Dataset Information

NGC: lossless and lossy compression of aligned high-throughput sequencing data.

Publications

NGC: lossless and lossy compression of aligned high-throughput sequencing data.

Similar Datasets

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