Identification of DNA nucleotides by conductance and tunnelling current variation through borophene nanogaps

Jena, Milan Kumar; Pathak, Biswarup

doi:10.1039/d2cp02093a

Cited by 7 publications

(6 citation statements)

References 70 publications

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“…To broaden our perspective, we have also delved into the interaction energy (E i ) for each nucleotide within C 3 N nanogap at their energetically most favorable configurations and compared with previously reported nanogap systems, 39,67,68 as illustrated in Figure S13a , where k B and T represent the Boltzmann constant and temperature). Therefore, the uniqueness of the E i and τ values can serve as crucial parameters for single nucleotide identification.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Further, the integration of the transmission function [T( E , V b )] entering from the left (L) to the right (R) electrode with an applied finite bias voltage ( V b ) provides current [ I ( V b )] as outlined in the following equation: , I false( V normalb false) = 2 e h ∫ T false( E , V normalb false) false{ f normalL ( E − μ L ) − f normalR ( E − μ R ) false} d E where f L ( E –μ L ) and f R ( E –μ R ) are the Fermi–Dirac distribution of electrons in the left and right electrodes, respectively. , …”

Section: Methodsmentioning

confidence: 99%

“…where f L (E−μ L ) and f R (E−μ R ) are the Fermi−Dirac distribution of electrons in the left and right electrodes, respectively. 49,55 Machine Learning. For ML application, we have utilized eight algorithms: k-nearest neighbors (KNN), 56 support vector machine (SVM), 57 decision tree classifier (DTC), 58 random forest classifier (RFC), 59 logistic regression (LR), 60 Gaussian nai ̈ve bayes (GNB), 61 Gaussian process classifier (GPC), 62 and stochastic gradient descent (SGD) 63 as available in open-source Scikit-learn 64 library with Python 3.10.…”

mentioning

confidence: 99%

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Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

Jena,

Mittal,

Pathak

2024

ACS Appl. Mater. Interfaces

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DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

Jena,

Mittal,

Pathak

2024

ACS Appl. Mater. Interfaces

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“…At present, nanopore/nanochannel sequencing is the most elegant in decoding the human genome, transcriptome, proteome, and epigenome with high sensitivity, selectivity, and accuracy. − Adsorption-based nanochannels are useful in detecting DNA/methylated DNA with controlled orientational fluctuations. − One major advantage of these nanochannels is that they are highly flexible and robust, in terms of shape and size and enable modifiable surface properties, depending on the required function. Because of the outstanding mechanical and electronic properties, a significant contribution has been made in exploring the 2D solid-state nanomaterials for biosensing applications. − …”

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confidence: 99%

Decoding Both DNA and Methylated DNA Using a MXene-Based Nanochannel Device: Supervised Machine-Learning-Assisted Exploration

Mittal

Manna

Jena

et al. 2023

ACS Materials Lett.

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An important pursuit in medical research is to develop a fast and low-cost technique capable of sequencing the entire human genome (DNA) and epigenome (methylated DNA) de novo. Such a method would enable the advancement of personalized medicines and a universal cancer screening test. In this regard, we introduce a novel supervised machine learning (ML) approach for ultrarapid prediction of transmission function of DNA and methylated DNA nucleobases using a MXene-based nanochannel device. The proposed device can detect the targeted nucleobases with good transmission sensitivity. The random forest regression (RFR) model can predict the transmission function of each unknown nucleobase with root-mean-square error (RMSE) values as low as 0.16. Interestingly, if the machine is trained with the dataset of methylated DNA nucleobases, it can selectively identify all four DNA nucleobases with good accuracy. Therefore, our study demonstrates an effective approach for quick and accurate whole-genome and epigenome sequencing applications.

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“…The E i and τ are unique for each nucleotide, which could be considered an important parameter for single nucleotide identification. Additionally, we have presented a comparative picture of E i with the defected graphene 34 and borophene nanogaps 35 and observed that the E i values for germanene nanogap are more distinct enough to serve as an identifier of the individual nucleotide. Here, the calculated E i for the germanium-nucleotide-germanium electrode configuration is also noted to be lower than those of the defected graphene and borophene electrodes.…”

mentioning

confidence: 99%

Artificially Intelligent Nanogap for Rapid DNA Sequencing: A Machine Learning Aided Quantum Tunneling Approach

Jena,

Roy,

Mittal

et al. 2023

ACS Materials Lett.

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Electrical identification of single DNA nucleotides with solid-state nanopore/nanogap and quantum transport technology offers a new paradigm in the field of DNA sequencing and has the potential to supersede existing techniques. However, the overlapping of fingerprint electric conductance signals due to the similar size and comparable frontier orbital energy levels of nucleotides has been a major impediment to identifying them with high accuracy. Herein, a synergistic approach combining the quantum transport method and machine learning algorithms has been devised to achieve the high-precision identification of DNA nucleotides. A model germanene nanogap is investigated for single-nucleotide-based DNA sequencing by calculating the transmission function and current−voltage characteristics. With the transmission function data sets, the Random Forest Classifier algorithm identified all four nucleotides and also demonstrated that binary, ternary, and quaternary combinations of nucleotides could also be classified with a high degree of precision, F1 score, and accuracy. The interelectrode distance analysis illustrates that transmission functions are sensitive to the electrode-nucleotide coupling effect and that the ML classifier can extrapolate the information during classification. Our findings provide a guide to the ML application on the nanogap device to achieve fast, cost-effective, and single-shot nucleotide identification.

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Identification of DNA nucleotides by conductance and tunnelling current variation through borophene nanogaps

Abstract: Rapid and inexpensive DNA sequencing is critical to biomedical research and healthcare for the accomplishment of personalized medicine. Solid-state nanopores and nanogaps have marshaled themselves in the fascinating paradigm of...

Cited by 7 publications

References 70 publications

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

Decoding Both DNA and Methylated DNA Using a MXene-Based Nanochannel Device: Supervised Machine-Learning-Assisted Exploration

Artificially Intelligent Nanogap for Rapid DNA Sequencing: A Machine Learning Aided Quantum Tunneling Approach

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