Using computing for data handling in bioinformatics

The ability to store, access and share data is favoured by researchers and encourages a more interconnected network to scientific research. Cloud systems allow for scalability and flexibility, where the researcher benefits by being able to perform complex analysis on significantly large datasets. The cost of cloud computing can be significantly lower than traditional modes of computing, making it an attractive option for the field of bioinformatics.

Here, we’ll explore the impacts of cloud computing on the field of bioinformatics and consider the scope of its potential to shape modern research.

The traditional method

Traditional computing models such as centralised computing (a single server that processes and distributes information across a network of users) often requires large upfront costs for hardware, which often come with resource allocations due to the complex web of dependencies.

These dependencies can include licences and continuous maintenance of the hardware and software. These also usually contain rigid terms and conditions which affect scalability of the service and the cost.

Using a cloud solution

The alternative, cloud computing, is an internet-based model allowing access from remote servers.

Cloud providers employ techniques that respond to researchers use of their product, meaning that the facility can automatically scale the service as needed. This allows researchers to quickly and easily increase their computing resources when needed to process large datasets, and then scale back down when the processing is complete, reducing costs and increasing efficiency.

Cloud computing also offers flexibility in data handling, as data can be accessed from anywhere, at any time, using a variety of devices. Data that was previously inaccessible to all collaborating researchers becomes accessible, making it easier to collaborate and share data. Researchers can work remotely, increasing productivity, efficiency and inclusivity in the field and a collaborative approach to research in bioinformatics means that resources are pooled efficiently.

The increased access to the data via cloud computing means that data sets can be very large, which enhances the value and interpretations of the data. The inclusive aspect of cloud computing increases the reach of the research among researchers, giving opportunities for new ideas and insights to be shared via the data on the cloud. This supports a multidisciplinary and interdisciplinary approach to research and cloud computing acts as a conduit of knowledge between researchers.

In addition, cloud computing offers a variety of tools and resources for data handling in bioinformatics. Cloud-based platforms, such as Amazon Web Services and Microsoft Azure, offer a wide range of tools and services for data storage, processing, and analysis. These platforms provide researchers with a comprehensive set of tools to manage and process large volumes of data, from data storage and processing to data visualisation and analysis. Within the field of bioinformatics these tools can include genome assembly, gene expression analysis, database design and machine learning algorithms to support their research.

The challenges

However, there are also challenges to using cloud computing for data handling in bioinformatics. One of the primary concerns is data security and privacy. Researchers need to ensure that their data is stored securely and that only authorised individuals have access to it.

Additionally, researchers need to make sure that their data is backed up regularly to prevent data loss in the event of a security breach or other issue. Reputable cloud computing providers are fully compliant with ISO/IEC 27001, which lays out the need to consider data security and risk management for users of their services. The providers will establish systems, policies and procedures to enhance data security and privacy and the effectiveness of these plans will be regularly reviewed and compliance fully communicated.

Researchers will need to be mindful of the security needs of their data so the cloud providers can provide the necessary features. These features include but are not limited to data encryption, which applies to:

· data at rest and data in transit

· monitoring and logging tools – this applies to audit logs of access to the data, tracking of tasks performed on the data and monitoring alerts

· network security, firewalls and monitoring of traffic

· data backup and disaster recovery

Like platform services however, security can cost. Cloud computing suppliers are responsive to the increased popularity of their products and therefore increased costs are expected with increased security features.

In conclusion, cloud computing offers many benefits for data handling in bioinformatics including scalability and flexibility, access to a variety of tools and resources and the ability to work remotely and collaborate with other researchers. While there are some challenges to using cloud computing, these can be addressed with proper planning, implementation and budgeting. As the field of bioinformatics continues to generate large volumes of data, cloud computing will likely become an increasingly important tool for managing and processing this data and will continue to play a crucial role in advancing research in the field.

If you have any questions around cloud storage, our team of data and cloud experts and bioinformaticians are happy to help – just get in touch.