Revolutionizing Genomic Instrumentation: Accelerated Base Calling With Deep Learning For Real-Time Precision
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Abstract
As deep learning methods are increasingly used in genomic instruments' basic base calling procedure, their significance in the field of genomics has increased. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are used in this paradigm shift to decode complex genetic data. The ability of these neural networks to decipher picture and signal data produced by high-tech tools allows for the inference of the complex organization of the 3 billion nucleotide pairs that make up the human genome. The accuracy of sequencing reads is improved, and base naming is made possible more quickly after real-time data production, which has significant implications for genomics. This leads to a dramatic acceleration of the whole genomics workflow, from sample collection to the creation of Variant Call Format (VCF) files and final reports, ushering in a new age of speed and precision in genetic research.
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