Characterization of MinION nanopore data for resequencing analyses

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Nanopore sequencing, exemplified by Oxford Nanopore Technologies' MinION, provides long reads and rapid sequencing.
• Evaluating the utility of MinION data for resequencing applications is crucial for advancing genomic analyses.

Purpose of the Study:

• To assess the MinION's performance for resequencing by comparing its data quality and variant detection capabilities against Illumina and Pacific Biosciences technologies.
• To analyze the error profile and coverage distribution of MinION data to understand its strengths and limitations.

Main Methods:

• Utilized the largest MinION dataset to date, alongside data from Illumina and Pacific Biosciences platforms.
• Employed five distinct mapping approaches to analyze MinION reads.
• Evaluated sequencing error rates, error distribution patterns, and depth of coverage uniformity.

Main Results:

• MinION sequencing exhibits a global error rate of approximately 11%, predominantly insertions and deletions.
• Errors are non-random, occurring in specific nucleotide patterns, potentially leading to false-positive variant calls.
• Despite challenges in small variant discovery (1 false indel per 1-10kb at 40x coverage), MinION demonstrates uniform coverage, unbiased by GC-content or mappability.
• High sensitivity and specificity were achieved in detecting copy number variants, outperforming other sequencing methods.

Conclusions:

• MinION technology is currently challenging for accurate small-sized variant discovery due to its error profile.
• The uniform and unbiased nature of nanopore sequencing makes MinION highly suitable for copy number variant detection.
• Further improvements in nanopore sequencing accuracy are needed to broaden its application in variant discovery.