The human genome is complex, and artificial intelligence (AI) has stepped in to further decipher it. From DNA sequencing to identifying genetic mutations, the rapid development of AI is having a major impact on the medical industry and our understanding of the human genome.
Both interest and funding for genomics and AI have increased in recent years, with the latter being touted as one of the key technologies that can transform the healthcare industry. In fact, some companies and industries now rely on genomic data analysis for diagnostic decision-making and therapeutic research and development. This article details the current trends and challenges of employing AI systems to extract useful data from highly complex genomic data.
Bioinformatics data storage
First and foremost, it is important to understand how much data is being generated through genome sequencing. When the entire human genome is sequenced, approximately 100 GB of raw data will be generated. Furthermore, when natural language processing and deep learning applications and algorithms are used to sequence the genome, the amount more than doubles. While the cost of sequencing the human genome continues to fall, the amount of sequencing data is growing exponentially, with the cost of sequencing the human genome continuing to decline, and by 2025 it will take 40 EB (approximately 10 exabytes) to store all human genome data. 78 million GB). This requires 8 times more storage to store every word spoken in the history. result? Genomic analysis pipelines have a very hard time keeping up with these massive data levels and how to analyze them.
Harnessing the power of AI for genome analysis
The application of AI to the world of genomics, aptly referred to as AI-enabled genomics, has and has the potential to have many useful applications in healthcare. AI algorithms such as deep learning (DL) and machine learning (ML) are used in genomic analysis to process and interpret large amounts of genetic data. In addition to identifying patterns, you can also classify genetic variation and make predictions based on data extracted from large datasets.
Accelerating genome sequence analysis
Sequencing a genome is not only a complex process, but also a computationally intensive process. There are many steps that need to be taken to identify genetic variations within the human genome. AI models can quickly analyze epigenetic, gene expression, and genomic data to identify genetic variations and their potential impact on DNA.
Furthermore, it is superior to traditional genomic analysis methods in many ways, including biomarker discovery, personalized medicine, identification of novel mutations, efficient analysis, and improved accuracy. Additionally, the entire process occurs in near real-time, speeding up the entire genomics workflow.
Artist's illustration of artificial intelligence (AI). This is an image of storing collected data in AI. This was created by Wes Cockx as part of the Visualizing AI project launched (Image source: Pexels.com)
Elucidation of genetic mutations
Variant calling is one of the most important steps in a genome sequencing project. This is when researchers identify differences between a reference genome and a patient sample, helping clinicians determine what specific genetic disease a critically ill patient may have.
It also helps researchers study populations to discover and identify new drug targets. This further lends itself to personalized genetics, where analysis of an individual's genetic makeup can be used to “personalize” treatment plans. Or it could help predict the probability that a particular person will develop a particular health condition.
Support for genome-wide association research
Phenotypes are observable traits in humans, such as blood type, eye color, and height, that are determined by both environmental factors and genotype, or genome makeup. Genome-wide association studies (GWAS) have identified millions of variants associated with these complex pathological phenotypes, making determining their functional impact a major challenge.
In this case, DL and ML algorithms have emerged as valuable tools to address this challenge. They were able to prioritize easily relevant functional variants within different pathogenic contexts. Furthermore, it may be possible to predict the effects of previously unobserved or rare mutations. Finally, publicly available data from GWAS can also be analyzed to prioritize causal variants associated with traits and diseases that have the potential to change the lives of people with immune-related diseases.
The future of genomics and AI
Advances in genome sequencing have clearly sparked a revolution in digital biology. AI algorithms are now a large part of preventive medicine and have demonstrated and continue to demonstrate the ability to accurately predict the effects of mutations on gene expression. Future advances could go as far as extending and enhancing these models to improve their usefulness and accuracy in different genetic contexts, which could also mean designing new models. .
AI is also playing an increasingly important role in driving personalized medicine by analyzing genomic data to identify an individual's unique disease risk, treatment response and optimal treatment approach. It has been shown to be useful in creating accurate and precise models to predict the development of diseases involving environmental factors and genetics.
Additionally, AI algorithms can help researchers gain a comprehensive understanding of complex biological processes, elucidate disease mechanisms, and potentially lead to drug discovery and identification of therapeutic targets.
