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Computer and Software Management for Nanopore Sequencing

Computer Requirements

To operate a Nanopore sequencing device, selecting the right computer and software is essential for smooth sequencing runs, data management, and analysis.

The Role of Computers in Nanopore Sequencing Workflows

Computers support every stage of the sequencing process, and their use can be divided into four general functions:

  1. To operate and control the sequencing device
    The computer is responsible for running the software (e.g., MinKNOW) that controls the sequencing device and monitors the sequencing process in real time. This task does not require a high-end machine; a laptop or desktop with a quad-core processor and 16 GB of RAM is sufficient.

  2. To provide storage for sequencing data
    Sequencing experiments can generate substantial amounts of data, even with small-scale experiments producing several gigabytes. As the number and complexity of experiments grow, so do storage needs. Solid-state drives (SSDs) are recommended due to their faster read/write speeds, which are crucial for smooth operation during sequencing runs. Ample storage space (1 TB or more) ensures you can manage and retain large datasets over time but a few hundred GB should be sufficient for small-scale applications.

  3. To perform basecalling
    Basecalling is the computational process that converts raw electrical signals from the sequencer into nucleotide sequences. While this can be done on most modern computers, a well-equipped machine with a discrete GPU significantly improves processing speed and accuracy. GPUs enable the use of advanced basecalling algorithms that enhance sequence quality, particularly for high-throughput applications.

  4. To conduct data analysis
    Data analysis involves tasks such as genome assembly, variant calling, and classification of sequencing reads. While these can require substantial computational resources, many free cloud-based platforms (e.g., EPI2ME, Galaxy, or CyVerse) reduce the need for high-end hardware. As a result, lower-specification computers can still be used effectively for data analysis, particularly when combined with cloud resources.

GPUs and basecalling

What is basecalling?
Basecalling is the process of converting raw electrical signals generated during sequencing into nucleotide sequences (A, T, G, C). High-quality basecalling enhances the accuracy of downstream analyses.

Using GPUs for basecalling
While basecalling can be performed without a GPU, access to GPUs significantly accelerates the process and supports the highest-quality basecalling algorithms. Modern discrete GPUs, such as NVIDIA GeForce RTX or equivalent, improve performance dramatically.

Accessing GPUs

  • JetStream2 and ACCESS-CI: Free access to GPU resources is available through these academic platforms. While powerful, they require some technical expertise to set up and use effectively. Learn more about JetStream2.
  • Amazon Web Services (AWS): Provides scalable GPU resources for basecalling and data analysis. However, these services incur hourly costs, depending on the instance type.

No GPU? No problem!
If you do not have access to a GPU, MinKNOW includes CPU-based basecalling that is sufficient for many educational and smaller-scale projects. However, processing times will be longer, and throughput may be reduced.

Matching computer resources to workflow needs

The computer used to run the MinION can be a high-performance machine to manage device operation, data storage, and basecalling efficiently. However, student computers used for limited or cloud-based data analysis tasks don’t need to meet such demanding requirements. Cloud platforms provide cost-effective ways to leverage advanced computational tools without requiring expensive hardware, ensuring accessibility for educators and students.

Minimum System Requirements

Oxford Nanopore devices, such as the MinION Mk1B and Mk1D, have specific requirements to ensure reliable operation. At a minimum, your computer should meet the following specifications:

  • Operating system:
    • Windows 10 or later (64-bit)
    • macOS 12 Monterey or later (Apple M2/M3/M4 series or Intel processors)
  • Processor:
    • Quad-core CPU (Intel i5/i7 or AMD equivalent) or Apple M2/M3/M4 series.
  • RAM:
    • At least 16 GB for efficient operation
  • Storage:
    • 500 GB SSD for data handling and storage
  • USB Ports:
    • One USB 3.0 port for device connection
  • Graphics:
    • Integrated graphics are sufficient for basic operations; discrete GPUs recommended for intensive tasks (see below for details on GPU use).

Warning: Do your homework - computer specifications change frequently

Please check MinION IT requirements or contact Nanopore support before making a purchase. This guide is not a replacement for doing your own homework!

For optimal performance, especially when handling large datasets or performing local basecalling, consider systems with higher specifications. Modern laptops such as the Apple MacBook Pro (M2/M3/M4 series) or high-performance Windows laptops meet these needs. The following have appeared on Nanopore educational resources as potential choices.

Data Storage in Nanopore Sequencing Workflows

Data storage is a critical aspect of Nanopore sequencing workflows, as even small experiments can produce several gigabytes (GB) of data. Proper storage solutions are essential for managing this data efficiently and ensuring it is secure and accessible for analysis and archiving. Both external solid-state drives (SSDs) and cloud-based storage options are options, each with advantages and limitations. Investing in appropriate storage solutions and a solid backup strategy, educators and researchers can effectively manage their sequencing data, ensuring its security, accessibility, and readiness for downstream analysis or publication.

External Solid-State Drives (SSDs)

External SSDs offer fast, reliable, and portable storage solutions, making them an excellent choice for sequencing workflows. The high read/write speeds of SSDs are especially important during sequencing runs, where real-time data transfer is required. Their durability and portability make them suitable for laboratory and fieldwork applications.

Recommendations for external SSDs

  • Capacity: A minimum of 500 GB is recommended for basic projects, but 1 TB or larger is ideal, especially for labs handling multiple datasets or larger-scale experiments.
  • Performance: Drives with high read/write speeds, such as NVMe or USB 3.2, are highly recommended to ensure efficient data handling during sequencing and analysis.

Cloud-Based storage solutions Cloud storage provides scalable and convenient options for managing sequencing data, particularly for collaborative or remote projects. Platforms like Google Drive, Dropbox, and AWS S3 allow users to upload and share data without relying on physical storage devices.

Considerations for cloud storage

  • Capacity and costs: While many platforms offer free storage tiers, these often have file size limits that can be restrictive for large sequencing datasets. Paid plans with higher storage capacities may be necessary as data volume increases.
  • Accessibility: Cloud solutions facilitate collaboration by enabling remote access and data sharing with colleagues or collaborators.
  • Backup: Cloud platforms can serve as a secondary or backup solution to complement local storage, ensuring data redundancy.

Data backup and archiving

For important datasets, particularly those intended for publication, a robust data backup strategy is essential. Backing up data on multiple devices or platforms minimizes the risk of data loss due to hardware failure or accidental deletion. Consider the following practices:

  • Use external SSDs for local backups and a cloud storage platform for off-site redundancy.
  • Regularly verify that backups are complete and accessible.
  • Implement a naming and organization strategy to ensure datasets are easy to locate and reference.

Important caveats and practices

While external and cloud-based storage solutions are effective, users should also consider:

  • Data privacy: Sensitive datasets, especially in clinical or ecological research, must comply with data privacy regulations.
  • FAIR Data management: Adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles is encouraged to promote data stewardship and reuse.

Software Requirements

Video: How to run your sequencing device and get started with data analysis

At a minimum, MinKNOW and EPI2ME software will be needed for Nanopore sequencing. In the bioinformatics section of this guide, we will provide and review additional information.

MinKNOW

MinKNOW is the primary software for operating Nanopore sequencing devices. It controls the sequencing hardware, monitors flow cell performance, and performs real-time basecalling.

  • Download: Available after registering an Oxford Nanopore account.
  • Compatibility: Pre-installed on MinION Mk1C and Mk1D; compatible with Windows and macOS systems for Mk1B and Mk1D.

EPI2ME

EPI2ME is a cloud-based platform for data analysis. It complements MinKNOW by providing tools for downstream tasks, such as read classification and genome assembly. EPI2ME is suitable for users with limited local computing resources.

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