Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using...

Area of Science:

• Genomics
• Bioinformatics
• Next-Generation Sequencing

Background:

• Low sequence diversity in libraries (e.g., from microRNA, Hi-C, barcoding) challenges Illumina sequencing.
• Illumina's cluster calling algorithm struggles with low diversity, causing low data quality and sample bleeding (misassignment).
• These issues are critical in clinical applications and rare variant detection.

Purpose of the Study:

• To address data quality issues and sample bleeding in low-diversity sequencing on Illumina platforms.
• To introduce and validate a novel software modification, the Long Template Protocol.
• To explore and compare alternative strategies for sequencing low-diversity samples.

Main Methods:

• Development and application of a Long Template Protocol software modification.
• Experimental validation comparing the Long Template Protocol with standard protocols.
• Evaluation of alternative strategies: barcode design, sample mixing guidelines, cluster density adjustments, and rescue strategies (re-hybridization, re-sequencing, image analysis).

Main Results:

• The Long Template Protocol significantly enhances data quality for low-diversity samples compared to standard protocols.
• The protocol substantially reduces sample bleeding across all sample types.
• Alternative strategies, including optimized barcode design and stringent sample mixing/density guidelines, also showed promise.

Conclusions:

• The Long Template Protocol offers a robust solution for improving low-diversity sequencing data quality and accuracy.
• The protocol effectively mitigates sample bleeding, crucial for clinical and research applications.
• A combination of protocol modifications and strategic experimental design is key to overcoming low-diversity sequencing challenges.