Sequencing error profiles of Illumina sequencing instruments

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Advancements in sequencing technology have led to widespread use of various instruments.
• Previous quality assessments focused on controlled conditions, not real-world public datasets.

Purpose of the Study:

• To develop a method for retroactively determining error rates in public sequencing datasets.
• To evaluate sequencing data quality across different Illumina instruments and identify factors influencing accuracy.

Main Methods:

• Utilized read overlaps inherent in many sequencing libraries to develop a novel quality assessment method.
• Analyzed 1943 public datasets from seven different Illumina sequencing instruments.

Main Results:

• More expensive platforms (HiSeq, NovaSeq) generally exhibit lower error rates and less variation.
• Significant variation in accuracy exists within individual platforms, heavily influenced by experimental factors.
• Identified sequence context, specifically preceding base bias, and noted instrument-specific patterns.
• Observed unexpected similarities in preceding-base bias exceptions between HiSeq X Ten and NovaSeq 6000.

Conclusions:

• Sequencing data quality is highly dependent on specific experimental circumstances, not solely instrument type.
• Individual evaluation of each sequencing experiment's quality is crucial for reliable genomic analysis.
• The developed method provides a valuable tool for assessing the quality of existing public sequencing data.