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Related Concept Videos

RNA-seq03:21

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Related Experiment Video

Updated: Dec 14, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Performance evaluation of lossy quality compression algorithms for RNA-seq data.

Rongshan Yu1, Wenxian Yang1,2, Shun Wang3

  • 1Department of Computer Science, Xiamen University, Xiamen, 316005, China.

BMC Bioinformatics
|July 22, 2020
PubMed
Summary
This summary is machine-generated.

Lossy quality value compression effectively reduces RNA sequencing (RNA-seq) data size but can impact analysis results. Careful selection of compression tools and levels is crucial for downstream RNA-seq studies.

Keywords:
Base qualityLossy compressionRNA-seq

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Area of Science:

  • Bioinformatics
  • Genomics
  • Data Compression

Background:

  • High-throughput sequencing generates massive genomic data, necessitating efficient storage and transmission.
  • Lossy compression, particularly for base quality values, offers superior compression performance over lossless methods for genomic data.
  • The applicability of lossy compression algorithms to RNA sequencing (RNA-seq) data is not well-established.

Purpose of the Study:

  • To evaluate the impact of lossy quality value compression on common RNA-seq data analysis pipelines.
  • To determine the effectiveness of lossy compression in reducing RNA-seq data size.
  • To identify potential adverse effects of lossy compression on RNA-seq analysis outcomes.

Main Methods:

  • Evaluation of lossy quality value compression across various RNA-seq data analysis pipelines (expression quantification, transcriptome assembly, variant detection).
  • Utilized RNA-seq data from diverse species and sequencing platforms.
  • Assessed the influence of different compression algorithms and levels on analysis results.

Main Results:

  • Lossy quality value compression significantly improves RNA-seq data compression, achieving 1.2-3 times greater size reduction than lossless methods.
  • Impacts of lossy compression vary across pipelines; HISAT2-based alignment was most affected.
  • STAR-based pipelines showed minimal effects on expression quantification and assembly, but variant detection was affected by both aligners, with less impact using STAR.

Conclusions:

  • Lossy quality value compression is a viable strategy to reduce RNA-seq data storage and transmission burdens.
  • Careful selection of compression tools and levels is essential to mitigate potential adverse effects on downstream RNA-seq analysis.
  • The choice of compression method should align with the specific requirements of the analysis pipeline to ensure result integrity.