Machine Learning-Guided Systematic Search of DNA Sequences for Sorting Carbon Nanotubes

ACS NANO(2022)

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The prerequisite of utilizing DNA in sequence-dependent applications is to search specific sequences. Developing a strategy for efficient DNA sequence screening represents a grand challenge due to the countless possibilities of sequence combination. Herein, relying on sequence-dependent recognition between DNA and single-wall carbon nanotubes (SWCNTs), we demonstrate a method for systematic search of DNA sequences for sorting single-chirality SWCNTs. Different from previously documented empirical search, which has a low efficiency and accuracy, our approach combines machine learning and experimental investigation. The number of resolving sequences and the success rate of finding them are improved from similar to 10(2) to similar to 10(3) and from similar to 10% to >90%, respectively. Moreover, the resolving sequence patterns determined from 5-mer and 6-mer short sequences can be extended to sequence search in longer DNA subspaces.
carbon nanotubes, DNA, machine learning, sequence selection, chirality sorting
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