Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based...

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Accurate quantification of DNA sequences is crucial for functional genomics.
• Current methods using PCR amplification and next-generation sequencing can introduce significant quantitative errors.
• There is a need for precise and reproducible DNA quantification techniques.

Purpose of the Study:

• To introduce REcount, a novel PCR-free method for direct DNA sequence tag quantification.
• To evaluate the accuracy and reproducibility of REcount compared to existing methods.
• To investigate biases in next-generation sequencing data and their economic impact.

Main Methods:

• Developed REcount, a PCR-free direct counting methodology.
• Validated REcount by quantifying defined plasmid pools.
• Compared REcount measurements with droplet digital PCR data.
• Analyzed clustering biases across different Illumina sequencers.

Main Results:

• REcount demonstrates high accuracy and reproducibility in quantifying DNA sequences.
• The method effectively bypasses PCR-induced quantitative errors.
• New insights into molecule length-related clustering biases in Illumina sequencing were revealed.
• The impact of these biases on data interpretation and generation costs was illustrated.

Conclusions:

• REcount offers a reliable and accurate alternative for DNA quantification in functional genomics.
• The findings highlight the importance of addressing sequencing biases for robust data analysis.
• REcount has implications for improving the efficiency and cost-effectiveness of next-generation sequencing experiments.