Dataset Information

Impact of lossy compression of X-ray projections onto reconstructed tomographic slices.

ABSTRACT: Modern detectors used at synchrotron tomographic microscopy beamlines typically have sensors with more than 4-5 mega-pixels and are capable of acquiring 100-1000 frames per second at full frame. As a consequence, a data rate of a few TB per day can easily be exceeded, reaching peaks of a few tens of TB per day for time-resolved tomographic experiments. This data needs to be post-processed, analysed, stored and possibly transferred, imposing a significant burden onto the IT infrastructure. Compression of tomographic data, as routinely done for diffraction experiments, is therefore highly desirable. This study considers a set of representative datasets and investigates the effect of lossy compression of the original X-ray projections onto the final tomographic reconstructions. It demonstrates that a compression factor of at least three to four times does not generally impact the reconstruction quality. Potentially, compression with this factor could therefore be used in a transparent way to the user community, for instance, prior to data archiving. Higher factors (six to eight times) can be achieved for tomographic volumes with a high signal-to-noise ratio as it is the case for phase-retrieved datasets. Although a relationship between the dataset signal-to-noise ratio and a safe compression factor exists, this is not simple and, even considering additional dataset characteristics such as image entropy and high-frequency content variation, the automatic optimization of the compression factor for each single dataset, beyond the conservative factor of three to four, is not straightforward.

SUBMITTER: Marone F

PROVIDER: S-EPMC7467350 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Impact of lossy compression of X-ray projections onto reconstructed tomographic slices.

Marone Federica F Vogel Jakob J Stampanoni Marco M

Journal of synchrotron radiation 20200728 Pt 5

Modern detectors used at synchrotron tomographic microscopy beamlines typically have sensors with more than 4-5 mega-pixels and are capable of acquiring 100-1000 frames per second at full frame. As a consequence, a data rate of a few TB per day can easily be exceeded, reaching peaks of a few tens of TB per day for time-resolved tomographic experiments. This data needs to be post-processed, analysed, stored and possibly transferred, imposing a significant burden onto the IT infrastructure. Compre ...[more]

PMID: 32876608

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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

Impact of lossy compression of X-ray projections onto reconstructed tomographic slices.

Publications

Impact of lossy compression of X-ray projections onto reconstructed tomographic slices.

Similar Datasets

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