The impact of quality filter for RNA-Seq

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Large-scale sequencing platforms have revolutionized DNA analysis since 2005.
• RNA sequencing (RNA-Seq) is crucial for gene expression analysis, but data quality and quantity impact reliability.
• RNA depletion and quality filters are used to enhance RNA-Seq accuracy, yet can influence expression profiles.

Purpose of the Study:

• To analyze the impact of varying quality filter stringency on RNA-Seq data from different sequencing platforms.
• To evaluate how quality filtering affects the identification of differentially expressed genes in microbial datasets.

Main Methods:

• RNA-Seq data from Corynebacterium pseudotuberculosis (SOLiD), Microcystis aeruginosa, and Kineococcus radiotolerans (Illumina) were analyzed.
• Different quality filter values (e.g., QV20, QV30) were applied to assess read removal and its effect on gene expression.
• Differential gene expression analysis was performed using a reference-based approach.

Main Results:

• Up to 47.9% of SOLiD reads were removed with QV20; 15.85% of K. radiotolerans reads were removed with QV30.
• Illumina data with the stringent QV30 filter identified 69 unique differentially expressed genes.
• Stricter filters generally increased the number of unique differentially expressed genes across all tested datasets, including M. aeruginosa.

Conclusions:

• Quality filters significantly influence RNA-Seq expression profiles, regardless of sequencing technology accuracy.
• The choice of quality filter stringency is critical for accurate interpretation of RNA-Seq experimental results.
• Reference-based RNA-Seq analysis outcomes are sensitive to data preprocessing steps like quality filtering.