Multi-perspective quality control of Illumina RNA sequencing data analysis

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Quality control (QC) is essential for reliable RNA sequencing (RNA-seq) results.
• Current QC practices are often limited or insufficient.
• Neglecting QC can compromise downstream analyses and conclusions.

Purpose of the Study:

• To present a comprehensive, multi-perspective strategy for RNA sequencing quality control.
• To highlight the importance of QC at each experimental stage.
• To address quality issues specific to mRNA, total RNA, and small RNA sequencing.

Main Methods:

• A four-stage QC framework: RNA quality, raw read data (FASTQ), alignment, and gene expression.
• Illustration of QC importance and recommended strategies.
• Discussion of quality issues across different RNA-seq types.

Main Results:

• Demonstration of a systematic approach to RNA sequencing QC.
• Identification of critical QC checkpoints throughout the RNA-seq workflow.
• Detailed examination of quality considerations for mRNA, total RNA, and small RNA sequencing.

Conclusions:

• Implementing a thorough, multi-stage QC strategy is crucial for robust RNA sequencing experiments.
• This comprehensive overview provides essential guidance for researchers.
• Ensuring quality at every step maximizes the reliability of RNA sequencing data.