Enhancing DNA Sequencing Workflow with AI-Driven Analytics
DOI:
https://doi.org/10.36676/jrps.v15.i3.1484Keywords:
DNA sequencing, AI-driven analytics, machine learning, deep learning, next-generation sequencing, data processing, personalized medicine, scalability, base calling accuracyAbstract
The rapid advancements in DNA sequencing technologies have revolutionized genomics, enabling a deeper understanding of genetic information and its implications in various fields such as medicine, agriculture, and evolutionary biology. However, the exponential increase in sequencing data presents significant challenges in terms of data management, analysis, and interpretation. Traditional methods often fall short in handling the complexity and volume of data generated, necessitating the integration of advanced technologies like Artificial Intelligence (AI) to optimize the DNA sequencing workflow.
AI-driven analytics offer transformative potential in enhancing DNA sequencing workflows by automating data processing, improving accuracy, and accelerating the pace of discovery. This abstract explores how AI can be integrated into various stages of the DNA sequencing process, including data preprocessing, alignment, variant calling, and downstream analysis. The integration of AI algorithms, such as machine learning and deep learning models, can streamline these processes by reducing manual intervention and minimizing errors. For instance, AI can enhance base calling accuracy, identify rare variants, and predict phenotypic outcomes with higher precision than traditional methods.
The AI-driven approach in DNA sequencing is particularly beneficial in handling the challenges posed by next-generation sequencing (NGS) technologies. These technologies generate massive amounts of data that require efficient processing and interpretation. AI algorithms can be trained on large datasets to recognize patterns and anomalies that may be overlooked by human analysts. This capability is crucial in identifying novel mutations, understanding complex gene interactions, and drawing meaningful conclusions from vast genomic datasets.
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