Chemists have now synthesized new kinds of DNA that add nucleotides to the four standard nucleotides (guanine, adenine, cytosine, and thymine) found in standard Terran DNA. Such “artificially expanded genetic information systems” are today used in molecular diagnostics; to support directed evolution to create medically useful receptors, ligands, and catalysts; and to explore issues related to the early evolution of life. Further applications are limited by the inability to directly sequence DNA containing nonstandard nucleotides. Nanopore sequencing is well-suited for this purpose, as it does not require enzymatic synthesis, amplification, or nucleotide modification. Here, we take the first steps to realize nanopore sequencing of an 8-letter “hachimoji” expanded DNA alphabet by assessing its nanopore signal range using the MspA (Mycobacterium smegmatis porin A) nanopore. We find that hachimoji DNA exhibits a broader signal range in nanopore sequencing than standard DNA alone and that hachimoji single-base substitutions are distinguishable with high confidence. Because nanopore sequencing relies on a molecular motor to control the motion of DNA, we then assessed the compatibility of the Hel308 motor enzyme with nonstandard nucleotides by tracking the translocation of single Hel308 molecules along hachimoji DNA, monitoring the enzyme kinetics and premature enzyme dissociation from the DNA. We find that Hel308 is compatible with hachimoji DNA but dissociates more frequently when walking over C-glycoside nucleosides, compared to N-glycosides. C-glycocide nucleosides passing a particular site within Hel308 induce a higher likelihood of dissociation. This highlights the need to optimize nanopore sequencing motors to handle different glycosidic bonds. It may also inform designs of future alternative DNA systems that can be sequenced with existing motors and pores.
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Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification
Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations. Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors. Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized. Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology.
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- Award ID(s):
- 1761839
- NSF-PAR ID:
- 10464623
- Date Published:
- Journal Name:
- Frontiers in Genetics
- Volume:
- 14
- ISSN:
- 1664-8021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fgene.2023.1154120
