Nanopore sequencing

Nanopore sequencing

A DNA molecule passing through a nanopore can produce a signal that is interpreted as sequence information. Source: Wikimedia Commons, CC BY 4.0.

Nanopore sequencing is a single-molecule sequencing approach in which DNA or RNA molecules pass through nanopores and produce signal changes that are computationally converted into sequence information. In practical systems, a motor protein controls molecular movement, many pores operate in parallel, and neural-network basecallers translate raw signal into reads.

The scientific value of nanopore sequencing comes from four connected properties: long reads, real-time acquisition, direct electrical sensing, and portable instrumentation. These properties make it useful for genome assembly, structural-variant analysis, microbial surveillance, outbreak response, environmental sequencing, transcriptomics, and direct detection of some nucleotide modifications.

Principle

A nanopore sequencing measurement has three coupled layers:

• Molecular layer - a DNA or RNA strand is prepared with adapters and is guided through a pore by an enzyme or motor protein.
• Physical layer - bases or short k-mer contexts inside the sensing region modulate ionic current or another measurable signal.
• Computational layer - signal processing and basecalling convert time-series data into reads, quality scores, and downstream analysis files.

A single current level is usually not assigned to one isolated base. The measured signal depends on a local sequence context, pore chemistry, motor speed, temperature, buffer, electronics, and model version. This is why basecalling and modified-base detection are model-based tasks rather than simple lookup tables.

Workflow from sample to result

Stage	Main question	Typical output	Quality checks
Sample and library preparation	Is the nucleic acid long, clean, and compatible with the kit?	Sequencing-ready library	Input mass, fragment length, inhibitors, adapter ligation efficiency
Sequencing run	Are enough active pores producing stable reads?	Raw signal and run metrics	Pore activity, read length distribution, yield, quality over time
Basecalling	What sequence best explains the raw signal?	FASTQ or BAM reads	Read accuracy, quality scores, model and chemistry compatibility
Alignment or classification	Where do reads originate?	Mapped reads, taxa, or contigs	Mapping quality, coverage, contamination, reference bias
Interpretation	What biological or technical conclusion is supported?	Variants, assemblies, methylation calls, reports	Controls, replication, thresholds, orthogonal validation when needed

Strengths

• Long reads can span repeats, structural variants, haplotypes, plasmids, and complex genomic regions.
• Real-time sequencing allows analysis during a run and supports rapid decision-making.
• Direct sensing can preserve information about some DNA or RNA modifications.
• Portable instruments make field, teaching, and low-infrastructure workflows possible.
• Flexible throughput allows small targeted runs and larger genome-scale projects.

Limitations and error sources

Nanopore sequencing should not be treated as automatically superior to short-read sequencing. Its suitability depends on the question, sample, required accuracy, cost, and analysis pipeline. Important limitations include:

• Raw read errors and systematic context-dependent errors.
• Sample-quality sensitivity, especially for long-read applications.
• Chemistry, pore, and model version dependence.
• Homopolymer and modified-base interpretation challenges in some contexts.
• Computational requirements for basecalling, alignment, assembly, and variant calling.
• Need for careful controls in clinical, regulatory, or high-consequence settings.

Applications

• Genome assembly - long reads help resolve repeats and structural complexity.
• Structural variation - long reads can span insertions, deletions, inversions, translocations, and repeat expansions.
• Microbial genomics - rapid identification, antimicrobial-resistance analysis, plasmid reconstruction, and outbreak tracking.
• Metagenomics and environmental DNA - real-time classification of mixed samples, with careful contamination control.
• Transcriptomics and direct RNA - isoform-level information and RNA modification signals in selected workflows.
• Epigenetics and base modifications - detection of methylation and other modifications when models and controls are appropriate.
• Adaptive sampling - real-time enrichment or depletion of targets by deciding whether to continue sequencing a molecule.

Data and file types

Raw and processed nanopore data may include POD5 or FAST5 signal files, FASTQ reads, BAM or CRAM alignments, sequencing-summary files, modified-base tags, and run reports. Good analysis practice records the instrument, flow cell, chemistry, kit, basecaller, model version, reference database, and command-line parameters.

Good practice

• Match the protocol to the biological question before optimizing yield.
• Use controls to separate library, pore, sample, and analysis effects.
• Report chemistry, basecaller, model version, and analysis parameters.
• Inspect both representative reads and aggregate statistics.
• Validate high-impact conclusions with independent evidence when possible.

Selected papers and resources

• Deamer D, Akeson M, Branton D. Three decades of nanopore sequencing. Nature Biotechnology. 2016.
• Jain M et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nature Biotechnology. 2018.
• Wick RR, Judd LM, Holt KE. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biology. 2019.
• Wang Y et al. Nanopore sequencing technology, bioinformatics and applications. Nature Biotechnology. 2021.
• Kovaka S et al. Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED. Nature Biotechnology. 2021.
• Oxford Nanopore Technologies. Dorado basecaller and related tools.

Related pages

• Nanopore technology
• Oxford Nanopore
• MinION
• Bioinformatics
• Machine learning
• Data analysis
• Event detection
• Glossary