Artificial intelligence

CLOUD COMPUTING IN GENOMIC RESEARCH

By: Kahishpreet Kaur1,2, Manvi Saini1,3, Siti Meilianawati 4, ALVI 4

1 CSE, Chandigarh College of Engineering and Technology, Chandigarh, India.

4 Department of computer science, Esa Unggul University, Indonesia

Email: 2[co22339@ccet.ac.in], 3[co22343@ccet.ac.in]

Abstract: Cloud computing has transformed genomic research, offering a revolutionary approach to studying genome structure, function, and mapping. Traditional methods relying on local infrastructure face challenges in scalability, collaboration, and data sharing. In contrast, cloud-based genomic research provides scalable storage, efficient high-performance computing, and secure data handling. Cloud computing’s elasticity optimized resource utilization, empowering instantaneous access to computational power for complex genomic analyses. This article explores the impact of cloud computing on genomic research, highlighting its role in overcoming traditional limitations and its potential to revolutionize healthcare and advance our understanding of the genetic basis of life.

Keyword: Genomic research, Cloud Computing, IaaS, PaaS, SaaS, Genomic data, Variant calling, Disease understanding, Machine learning

Introduction:

Genomic research is a field of science that deals involving the organization, operation, mapping, and modification of genomes. A genome represents the collection of genetic material found within a species. This research focuses on identifying the order of nucleotide bases in DNA, role of different genes in various biological processes, evolutionary relations to identify the genetic basis of specific traits, variations in individual DNA sequences, epigenomics etc.

Cloud computing offers computing services via the internet, computing resources without need for maintaining physical hardware and infrastructure. Resources can be dynamically allocated and reassigned based on the demand. [1]

Traditional Approach Towards Genomic Research:

In the traditional approach towards genomic research, the researchers often rely on local or on-premises data and they manage their own hardware to conduct analyses. [2] As the researchers maintain their own servers and storage systems, often investment is required in additional hardware which might lead to scalability limitations. Collaboration and data sharing among researchers can become challenging in the traditional approach as sharing large genomic datasets among geographically dispersed teams might involve manual transfer methods, leading to delays and potential security concerns. [3-5]

Limitations of Traditional Approach:

Traditional genomic research faces significant challenges, notably in scalability due to high upfront costs and complex processes involved in expanding computational power and storage. This limitation obstructs extensive data analysis and complex computations. [6] The substantial financial burden of establishing and maintaining infrastructure, including hardware procurement and maintenance, particularly affects smaller research institutions with limited budgets. The responsibility for upkeep detracts from researchers’ core activities. Additionally, difficulties in sharing data among dispersed teams lead to collaboration issues, delays, and security risks. Inefficient resource usage, along with data security concerns and compliance challenges, emphasizes the need for greater flexibility, a feature often lacking in traditional setups compared to cloud-based solutions. Figure 1 compares traditional approach and cloud-based approach for genomic research.

Cloud Based Approach Towards Genomic Research:

In the Cloud [7] Based approach, the researchers use cloud computing to scale resources up or down thus optimizing the costs. Here researchers can instantly access additional computational power to handle large-scale genomic analyses, allowing for efficient processing of extensive datasets and complex computations. Cloud platforms facilitate seamless collaboration and data sharing among researchers and institutions. Centralized storage and access controls enable secure sharing of genomic data among authorized collaborators globally, fostering real-time collaboration and analysis [8]. This approach also relieves researchers from the burden of managing and maintaining the physical infrastructure.

Fig 1: Difference between Traditional and Cloud-based Approach

Indispensable Role of Cloud Computing in Genomic Research:

Vast number of datasets are used in genomic research which are stored and operated on cloud platforms thereby providing scalable and secure storage solutions. Cloud platforms [9] facilitate easy sharing and collaboration among researchers, institutions, and globally distributed teams. By storing genomic data in the cloud, researchers can grant access to collaborators, fostering real-time collaboration and thus, it accelerates scientific discovery. Genomic research involves computation intensive tasks variant calling, alignment, and functional annotation. Cloud computing allows researchers to perform these analyses using high-performance computing resources without the need for significant upfront investments in specialized hardware. Researchers can easily scale their genomic analyses using cloud computing. Cloud [10] platforms host bioinformatic tools and pipelines designed specifically for genomic analysis. Researchers can leverage pre-configured environments to streamline their analyses.

Cloud computing provides an elastic computing model which is beneficial for handling large datasets during intensive analysis, ensuring optimal resource utilization.

It makes it easy to dynamically adjust computational resources, enabling researchers to seamlessly scale up or down based on the demands of their analysis. This elasticity allows for efficient allocation of resources precisely when needed, minimizing unnecessary costs and maximizing performance during peak computational loads. Additionally, the on-demand nature of cloud computing empowers researchers to access additional computing power instantaneously, facilitating quicker data processing and analysis without the constraints of physical infrastructure limitations. [11]

Cloud computing services can be categorized into three main models [12]:

Model Name

Functions

Infrastructure as a Service (IaaS)

Offers virtualized computational assets through the internet.

Users have the option to lease virtual machines, storage, and networking infrastructure with a pay-as-you-go arrangement.

Platform as a Service (PaaS)

Provides a platform encompassing hardware and software elements.

User can develop, and oversee applications without the need to navigate the intricacies of the foundational infrastructure.

Software as a Service (SaaS)

It can deliver software applications through the internet using a subscription-based model.

Users can utilize the software without concerns about managing the underlying infrastructure or maintenance of performance.

Table 1: Main Models of Cloud Computing Services

Genomic research utilizes multiple cloud computing models depending on the needs of tasks and requirements of research tasks. Cloud computing [13] models play a pivotal role in genomic research, responding dynamically to the diverse demands and intricacies inherent in various research tasks. IaaS emerges as the primary model, offering researchers the substantial computing power and storage capacity essential for large-scale data processing in tasks like genome sequencing and variant analysis. PaaS streamlines the development and deployment of data analysis applications, allowing researchers to focus on application management without dealing with underlying infrastructure complexities, thus enhancing efficiency. Although less prevalent, Software as SaaS models are occasionally employed for specific tools, such as collaborative research platforms and data visualization [14] tools, adding flexibility to manage research workflows and applications. In essence, the primary use of IaaS underscores its adaptability to genomic research’s computational demands, while the inclusion of PaaS and SaaS models provides supplementary advantages to specific aspects of the research process.

Revolutionizing Genomic Research Through Cloud Computing:

Cloud computing transforms genomic research by enhancing data storage, boosting high-performance computing, and ensuring secure, compliant data management, fostering efficiency and trust among researchers.

1. Scalable Storage Solutions:

Cloud computing [15] assumes a pivotal role, offering an adaptable and cost-effective storage infrastructure for vast genomic datasets. Ensuring secure storage and retrieval, it transcends local storage limitations and meets the challenges posed by exponential data growth with flexibility and efficiency.

2. High-Performance Computing (HPC):

Genomic analyses benefit immensely from the substantial computing power conferred by cloud platforms. Access to high-performance computing clusters [16] empowers researchers to parallelize tasks like variant calling and alignment, expediting scientific discoveries by efficiently processing large-scale genomic datasets.

3. Secure and Compliant Data Handling:

Paramount to genomic research in the cloud is the assurance of security and compliance. Cloud providers [17] institute robust security measures, adhering to regulatory standards, thereby ensuring data confidentiality and integrity. Comprehensive data handling practices within the cloud establish a secure foundation [18], engendering trust among researchers and stakeholders.

Fig 2: Approach to Simulate Cloud Computing through Genetic Algorithm.

Implications for Personalized Medicine and Disease Understanding:

1. Precision Medicine Acceleration:

Cloud computing accelerates [19] personalized medicine by swiftly analysing individual genetic data. Tailoring treatments based on genetic insights enhances effectiveness and diminishes side effects.

2. Augmented Disease Understanding:

Empowered by cloud computing, genomic research elucidates the genetic underpinnings of diseases. This knowledge identifies new treatment targets and pathways, paving the way for precise and efficient interventions. [20]

Future Scope:

Future developments in cloud-based genomic research will refine analytical tools using advanced machine learning and AI, providing deeper insights into disease mechanisms. Collaboration across diverse fields facilitated by cloud computing will drive innovative applications in personalized medicine. Integrating multi-omics data on cloud platforms will offer comprehensive biological insights for targeted therapies. Innovations in data security, scalability, accessibility aim to democratize global genomic research. Real-time data analysis could transform clinical practices, aiding prompt decisions and personalized treatments. Addressing ethical and regulatory challenges ensures responsible genomic data usage, maintaining public trust in cloud-based research ethics.

Conclusion:

At the crossroads of cloud computing and genomic research, a synergistic alliance is reshaping the landscape of precision medicine and disease comprehension. The efficient storage, processing, and analysis of genomic data within the cloud are pivotal in unlocking the enigmatic secrets encoded in our DNA. With each computational stride, cloud computing propels genomics into a new era of discovery, promising innovative solutions that will revolutionize healthcare and advance our understanding of the genetic basis of life.

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Cite As

Kaur K, Saini M, Meilianawati S, ALVI (2024) CLOUD COMPUTING IN GENOMIC RESEARCH, Insights2Techinfo, pp.1

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