Abstract
In the rapidly evolving field of genomics, our capacity to decipher genetic data encoded in DNA has been transformed by Next Generation Sequencing (NGS) technologies. These advanced technologies produce an enormous volume of data, posing substantial challenges in extracting meaningful biological insights. Artificial intelligence (AI) algorithms offer distinctive possibilities to unravel the biological information embedded in such extensive and intricate datasets. This review offers a synopsis of AI classifications and algorithms, elucidating how these techniques can be employed on sequencing data. Subsequently, a selection of the most typical, promising, or illustrative applications of AI on NGS data to tackle unresolved technical or biological issues are showcased.
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