A framework and an algorithm to detect low-abundance DNA by a handy sequencer and a palm-sized computer

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Detecting low-abundance DNA in contaminated samples is crucial for epidemiology and field research.
• Existing methods for selective DNA sequencing are often time-consuming and costly.
• Oxford Nanopore Technology's MinION offers selective sequencing via 'Read Until' but faces computational and algorithmic challenges.

Purpose of the Study:

• To develop a statistical model for selective DNA sequencing.
• To create an efficient, constant-time classifier for real-time DNA analysis.
• To address computational limitations in field-based genomic applications.

Main Methods:

• Developed a statistical model for modeling selective sequencing.
• Proposed an efficient constant-time classifier for background DNA profiles.
• Validated the model and classifier using mock and mixed DNA samples.

Main Results:

• Achieved approximately 500-fold amplification of a 100 kb target region (0.1% of the pool) in a mock sample.
• Demonstrated real-time processing of 26 queries per second on a low-cost computing device.
• Successfully amplified a specific 30-230 kb region of S. cerevisiae chromosome 1 with ~30-fold enrichment.

Conclusions:

• The proposed method enables efficient and precise selective DNA sequencing.
• The approach is practical for field research due to low computational requirements.
• The method allows dynamic adjustment of target regions during sequencing.