Error Rate of PacBio vs Nanopore: How Accurate Are Long-Read Sequencing Technologies

In the realm of genomics research, the error rate serves as a critical indicator of the reliability associated with sequencing technologies. Elevated error rates can result in false positive or false negative variant detections, consequently affecting the completeness and accuracy of genome assembly. PacBio and Nanopore are two prominent representatives of long-read sequencing technologies that employ distinct strategies for error rate management. PacBio significantly enhances accuracy through its circular consensus sequencing mode (HiFi), whereas Nanopore focuses on algorithmic optimization and hardware improvements (such as the R10 chip) to minimize systematic errors. This paper aims to conduct an in-depth analysis of the accuracy performance and potential areas for improvement of these two technologies, examining the sources of error, error rate comparisons, correction strategies, and practical implications.

Sources of Errors

The error rates of long-read sequencing technologies primarily stem from their unique detection principles and technical characteristics. PacBio and Nanopore exhibit significant distinctions in error types: PacBio’s errors are predominantly stochastic, largely associated with limitations in fluorescence signal detection, whereas Nanopore’s errors are systematic, mainly concentrated in current signal recognition biases within homopolymeric regions. This section provides a detailed analysis of the error origins and fundamental causes for both technologies.

PacBio: Stochastic Errors and Correction via Circular Consensus Sequencing (HiFi)

PacBio’s Single Molecule Real-Time (SMRT) technology relies on zero-mode waveguide (ZMW) holes and DNA polymerase for fluorescence signal detection. The primary sources of errors include:

1. Fluorescence Signal Misinterpretation: During DNA synthesis, the release of fluorescence-labeled dNTP signals may be disrupted by background noise or signal decay, leading to incorrect base interpretation.
2. Polymerase Kinetic Errors: Variability in the synthesis speed of DNA polymerase may cause timing discrepancies in fluorescence signals, increasing the stochastic error rate.
3. Correction Mechanism with HiFi Reads: HiFi sequencing generates consensus sequences through multiple passes, reducing the initial error rate from approximately 15% to less than 1%. This self-correcting capability significantly enhances data reliability, making it suitable for high-precision genome assembly and variant detection.

Error profile of Pacific Biosciences data. (Carneiro, M.O., et al., 2012)

Nanopore: Systematic Errors (e.g., Homopolymer Sequencing Bias) and Dependence on Algorithmic and Hardware Enhancements

Nanopore technology is based on current signal detection, with errors primarily arising from the following:

1. Homopolymeric Region Bias: In sequences with consecutive identical bases (e.g., AAAAA or TTTT), the subtle changes in current as they pass through the nanopore can lead to inaccurate base length recognition, serving as a major source of systematic error for Nanopore.
2. Nanopore Protein Stability: Structural changes in nanopore proteins during extended operation can impact the stability and consistency of current signals.
3. Hardware and Algorithm Optimization: The R10 chip employs a dual reader head design, greatly enhancing homopolymer region accuracy. Deep learning algorithms, such as Bonito and Guppy, optimize base calling through trained models, reducing systematic error rates.

Error rates of ONT reads before(c) and after(a, b) error correction. (Sahlin, K., et al., 2021)

Additionally, the error rate of Nanopore sequencing is considerably influenced by DNA quality. Laboratory data indicate that error rates vary by sample type-approximately 7% for bacterial and most fungal samples, 8% for insects, mammals, and crops, and up to 8-10% for species rich in secondary metabolites, such as certain medicinal plants and fungi. This variability is primarily attributed to differences in DNA purity and sample type.

Comparison of Error Rates

PacBio and Nanopore exhibit significant differences in both their initial error rates and post-correction accuracy, reflecting the fundamental characteristics and developmental trajectories of these technologies. PacBio substantially reduces stochastic errors through its circular consensus sequencing approach, whereas Nanopore enhances its systematic error correction capabilities via hardware advancements and algorithmic optimizations. This section delves into a detailed analysis across three aspects: differences in initial error rates, trends in technological improvements, and methodologies for enhancing post-correction accuracy.

Error distribution along raw PacBio reads (green) and raw ONT reads (purple) for substitutions (A, D), deletions (B, E) and insertions (C, F). (Dohm, Juliane C., et al., 2020)

Initial Error Rate Differences and Technological Improvement Trends

• The initial error rates for PacBio primarily arise from misinterpretations of fluorescence signals and stochastic errors during the polymerase synthesis process. By implementing the HiFi mode to achieve circular consensus sequencing, PacBio elevates post-correction accuracy to exceptionally high levels, significantly mitigating the impact of stochastic errors.
• In contrast, Nanopore’s initial error rates are predominantly concentrated in homopolymeric regions, characterized by current signal recognition biases with consecutive A/T bases. The R10 chip, featuring a dual-reader head design, enables double signal recognition for the same base, markedly improving accuracy in homopolymeric regions and reducing the initial error rate to a lower level.

Methods for Enhancing Post-Correction Accuracy

• PacBio employs circular consensus sequencing to produce consistent sequences, drastically lowering the initial error rate. The HiFi mode utilizes multiple sequencing passes to generate high-precision consensus sequences, making it suitable for high-accuracy genome assembly and variant detection.
• Nanopore enhances data accuracy through consensus sequence generation and deep learning algorithms. The R10 chip, combined with high-depth sequencing, can elevate consensus sequence accuracy to exceptionally high levels, achieving even higher precision under specific conditions.

When selecting a technology, it is crucial to balance research needs-such as high precision and real-time capabilities-with the characteristics of error rates to achieve optimal data quality and research outcomes.

Error Correction Strategies

PacBio and Nanopore employ distinct error correction strategies, reflecting their foundational design philosophies and paths of advancement. PacBio enhances data accuracy significantly through a self-correction mechanism based on iterative sequencing, whereas Nanopore relies on deep learning models and multi-tool integrated analysis to refine systematic error correction capabilities. This section provides a detailed introduction to the error correction strategies of both technologies and their practical applications.

PacBio: Self-Correction Based on Iterative Sequencing

The core of PacBio’s error correction strategy is its High Fidelity (HiFi) mode. Using Circular Consensus Sequencing (CCS), PacBio performs multiple sequencing passes on the same DNA molecule to produce high-precision consensus sequences. This approach effectively reduces the rate of stochastic errors, bringing the original error rate to an exceedingly low level.

Overview of the concatenation procedure using PacBio circular consensus sequencing (CCS). (Kanwar, N., et al., 2021)

Technical Advantages:

1. The consensus sequences generated by the HiFi mode exhibit extremely high accuracy, making them suitable for high-precision genome assembly and variant detection.
2. The self-correction mechanism simplifies data analysis by eliminating the reliance on external data or complex algorithms.

Application Scenarios:

1. Clinical-grade Genome Sequencing: The high accuracy of HiFi reads makes them ideal for detecting structural variants and rare mutations.
2. Complex Genome Assembly: By minimizing error rates, PacBio significantly enhances the resolution of complex repeat regions and structural variants.

Nanopore: Deep Learning Models and Multi-Tool Integrated Analysis

Nanopore’s error correction strategy is chiefly reliant on deep learning models and integrated analysis with multiple tools. By combining hardware upgrades (such as the R10 chip) with algorithmic refinements, Nanopore significantly reduces systematic error rates and enhances data reliability.

Technical Advantages:

1. Deep learning models (such as Bonito and Guppy) enable real-time base calling optimization, reducing error rates in homopolymeric regions.
2. Multi-tool integrated analysis (including Medaka and Pore-C) further enhances data accuracy by generating consensus sequences.

Application Scenarios:

1. Real-time Monitoring: The ability of deep learning models to support real-time base recognition is suitable for infectious disease monitoring and rapid response scenarios.
2. Portable Sequencing: Through multi-tool analysis, Nanopore demonstrates unique advantages in field or clinical point-of-care testing.

This analysis clearly elucidates the differences and strengths of PacBio and Nanopore in terms of error correction strategies. The choice of technology should be balanced with research needs-such as high precision and real-time capabilities-and the characteristics of the error correction mechanisms to achieve optimal data quality and research outcomes.

Practical Implications

The error rate is a central factor affecting the reliability of sequencing data and the accuracy of research conclusions. High error rates can lead to false positive or false negative results in variant detection, compromise the integrity of genome assembly, and even mislead subsequent biological interpretations. Developing effective quality control strategies tailored to the distinct error characteristics of PacBio and Nanopore is crucial for ensuring the reliability of research outcomes. The following analysis explores the potential risks of error rates on research conclusions and provides targeted quality control recommendations.

Potential Risks of Error Rates on Research Conclusions

1. False Positive and False Negative Results

• High error rates may lead to misreports (false positives) or missed reports (false negatives) in variant detection. For instance, in cancer genomics, false positives may incorrectly identify pathogenic mutations, while false negatives may miss critical driver mutations.
• Example: In rare disease research, data with higher error rates could result in misdiagnoses or overlooked potential therapeutic targets.

2. Errors in Genome Assembly

• Long-read data with high error rates may cause misalignment or omission of assembled fragments, particularly in complex repeat and structural variation regions.
• Example: In plant genome assembly, high error rates might lead to incorrect contig connections, impacting genome annotation and functional analysis.

3. Bias in Transcriptome Analysis

• In transcriptomic studies, error rates may affect the identification of alternative splicing events and the detection of RNA modifications, leading to misunderstandings of gene regulation mechanisms.
• Example: In RNA virus research, error rates might result in incorrect full-length transcript assembly, influencing studies on viral replication mechanisms.

Quality Control Recommendations

1. Quality Control Strategies for PacBio

• Utilize HiFi Mode: HiFi reads, generated through circular consensus sequencing, provide high-precision data suitable for studies requiring high accuracy.
• Integrate Short-Read Data: Combining PacBio long-read data with Illumina short-read data for hybrid assembly further enhances data reliability.
• Data Filtering: Remove low-quality reads (e.g., those with low consensus scores or short read lengths) prior to analysis to minimize the impact of error rates on results.

2. Quality Control Strategies for Nanopore

• Employ R10 Chip: The R10 chip significantly reduces error rates in homopolymeric regions and is recommended for studies demanding high-quality data.
• Generate Consensus Sequences: Improve data accuracy through high-depth sequencing (>50X) and consensus sequence generation tools such as Medaka.
• Algorithm Optimization: Use the latest deep learning models (e.g., Bonito) for base calling to mitigate the impact of systematic errors.

3. General Quality Control Measures

• Data Validation: Use independent experiments (e.g., PCR or Sanger sequencing) to validate critical variants or assembly results.
• Standardize Procedures: Establish standardized sequencing and data analysis workflows to ensure consistency and comparability across different data batches.
• Regular Equipment Calibration: Periodically calibrate and maintain sequencing equipment to ensure stability and accuracy in data generation.

Summary Table of Error Rate Impacts and Quality Control

Summary

As two leading representatives of long-read sequencing technologies, PacBio and Nanopore each exhibit distinct strengths and weaknesses in error rate management, error correction strategies, and application scenarios. PacBio achieves high-precision sequencing via its HiFi mode, making it ideal for research demanding high accuracy. In contrast, Nanopore’s real-time capabilities and portability offer unique value in field monitoring and rapid response settings.

Error Rates and Correction Strategies of PacBio and Nanopore Sequencing Technologies

Applications and Advantages of PacBio and Nanopore

PacBio excels in genome assembly, structural variant detection, and transcriptome analysis. Its HiFi mode generates high-precision data through circular consensus sequencing, which is particularly suited for parsing complex genomes and detecting high-confidence variants. Additionally, PacBio can directly detect DNA methylation and base modifications, providing high-resolution data for epigenetic studies. These capabilities find extensive applications in cancer genomics and rare disease research.

Nanopore, however, holds unique advantages due to its real-time data streaming, portability, and ultra-long read capabilities. Its real-time functionality supports infectious disease surveillance and rapid response efforts, having played vital roles during public health crises like the Ebola virus outbreak. The portability of the MinION device makes it apt for genomic sequencing in field settings, extreme environments such as polar regions, and even space, offering new tools for ecological and space biology research. In clinical point-of-care testing, Nanopore can swiftly diagnose pathogens or decipher patient genomic variations, supporting precision medicine initiatives. Furthermore, its direct RNA sequencing technology, which bypasses reverse transcription, allows for direct RNA sequence and modification reading, paving new avenues for functional genomics research.

If you want to learn more about PacBio and Nanopore sequencing technology, you can read the following articles:

• PacBio vs Oxford Nanopore: Which Long-Read Sequencing Technology is Right for Your Research?
• Overview of PacBio SMRT sequencing: principles, workflow, and applications
• Nanopore Sequencing: Principles, Platforms and Advantages

CD Genomics, as a leading sequencing service provider, offers comprehensive PacBio and Nanopore sequencing solutions. We recommend the most suitable sequencing technology based on clients’ research objectives and budget, ensuring high precision and reliability of the data. From sample preparation and sequencing to data analysis, CD Genomics provides end-to-end services, enabling clients to achieve research outcomes efficiently.

References

1. Carneiro, M.O., Russ, C., Ross, M.G. et al. Pacific biosciences sequencing technology for genotyping and variation discovery in human data. BMC Genomics 13, 375 (2012). https://doi.org/10.1186/1471-2164-13-375
2. Dohm, Juliane C., et al. "Benchmarking of long-read correction methods." NAR Genomics and Bioinformatics 2.2 (2020): lqaa037. https://doi.org/10.1093/nargab/lqaa037
3. Sahlin, K., Medvedev, P. Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis. Nat Commun 12, 2 (2021). https://doi.org/10.1038/s41467-020-20340-8
4. Kanwar, N., Blanco, C., Chen, I.A. et al. PacBio sequencing output increased through uniform and directional fivefold concatenation. Sci Rep 11, 18065 (2021). https://doi.org/10.1038/s41598-021-96829-z