Performance evaluation of lossy quality compression algorithms for RNA-seq data

BMC Bioinformatics
Rongshan YuShun Wang

Abstract

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. 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 proc...Continue Reading

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Datasets Mentioned

BETA
SRR1043300
SRR10509596
SRX4122949
SRR7216027

Methods Mentioned

BETA
exome sequencing
RNA-seq
genotyping

Software Mentioned

HISAT2
Linux OS
ScaleQC
Nextflow
GATK HC
samtools
accompany
cocktail toolkit
RNA
Illumina

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