A genome is a complete collection of DNA in an organism. All living species possess a genome, but they differ considerably in size. As a data-driven science, genomics extensively utilizes machine learning to capture dependencies in data and infer new biological hypotheses. Nonetheless, the ability to extract new insights from the exponentially increasing volume of genomics data requires more powerful machine learning models. By efficiently leveraging large data sets, deep learning has reconstructed fields such as computer vision and natural language processing. It has become the method of preference for many genomics modeling tasks, including predicting the influence of genetic variation on gene regulatory mechanisms such as DNA receptiveness and splicing. So here, we will understand DNA structure and how machine learning can be used to work with DNA sequence data.
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