Nucleotide sequence analysis8/6/2023 ![]() Accordingly, interpreting non-coding variants is a highly complex task that may require more than traditional data analysis. Moreover, non-coding variants may influence the 3D structure and the epigenome. That being said, non-coding variants may regulate different genes depending on the genomic 3D structure and the epigenome. Variants in coding regions can be interpreted based on knowledge of the particular gene function, which considerably simplifies the analysis. However, non-coding genetic variants are not as easy to interpret as the coding region genetic variants assigned to a known gene. Now that the first gap-less human genome has been completed, the next stage of research and analysis in this field will yield vast non-coding genetic data, which will in turn improve the diagnostic and therapeutic decision-making capabilities of AI systems that can be built on this as-of-yet untapped information. Most genetic variation associated with diseases locate in non-coding regions of the genome. Interpretation of Non-Coding Genetic Variation in a Three-Dimensional Context Below, we describe the recent development of AI systems for analyzing information from the non-coding regions in the genome (I), from a combination of different genomic and medical information (II), and from liquid biopsies and cfDNA that depend on interpreting genomic data from fragments of the overall genome (III). Recently developed AI systems significantly improve the accuracy of therapeutic and diagnostic predictions. Moreover, accurate therapeutic and diagnostic decisions may require integrating genomic data analysis with medical information and patient data.Īccordingly, AI systems, with their capacity for capturing intricate patterns within large data sets and combinations of different data modalities, could become powerful tools for therapeutic and diagnostic decision-making that can address some of the challenges posed by the human genome complexity. Information about these different genomic features may come from entirely different data modalities, such as DNA sequencing, imaging, and various biochemical assays. Taking full advantage of genomic data for therapeutic and diagnostic decision-making will require integrating the linear DNA sequence data of coding and non-coding regions, the 3D genomic structure information, and the epigenome. In this post-human genome sequence world, it is becoming increasingly clear that human disease and disease susceptibility are not only a consequence of a particular mutation causing a particular gene dysfunction, but are often a result of genetic variations in non-coding regions, the three-dimensional (3D) structure of the genome, and chemical modifications of the DNA and protein molecules that make up the genome (referred to as the “epigenome”). While completing the gap-less sequence of the human genome was a milestone for science, the complexity poses a considerable challenge for clinical use of the data, as we have previously discussed. The Challenge and Promise of Interpreting Complex Genomic Data This article provides an overview of current trends in using AI systems to meet the challenge of extracting clinically useful information from highly complex genomic data. The rapid development of artificial intelligence (AI) systems will significantly impact businesses that rely on genomic data analysis for research and development of therapeutics and diagnostic decision-making.
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