scholarly journals Picomolar Fingerprinting of Nucleic Acid Nanoparticles Using Solid-State Nanopores

ACS Nano ◽  
2017 ◽  
Vol 11 (10) ◽  
pp. 9701-9710 ◽  
Author(s):  
Mohammad Amin Alibakhshi ◽  
Justin R. Halman ◽  
James Wilson ◽  
Aleksei Aksimentiev ◽  
Kirill A. Afonin ◽  
...  
Keyword(s):  
ACS Nano ◽  
2021 ◽  
Author(s):  
Komal Sethi ◽  
Gabrielle P. Dailey ◽  
Osama K. Zahid ◽  
Ethan W. Taylor ◽  
Jan A. Ruzicka ◽  
...  

2019 ◽  
Vol 10 (7) ◽  
pp. 1953-1961 ◽  
Author(s):  
Zhentong Zhu ◽  
Ruiping Wu ◽  
Bingling Li

We adapt a solid-state nanopore for analyzing DNA assembly mixtures, which is usually a tougher task for either traditional characterization methods or nanopores themselves. A trigger induced nucleic acid amplifier, SP-CHA, is designed as a model. We propose an electrophoresis-gel like, but homogeneous, quantitative method that can comprehensively profile the “base-pair distribution” of SP-CHA concatemer mixtures.


Nano Letters ◽  
2009 ◽  
Vol 9 (8) ◽  
pp. 2953-2960 ◽  
Author(s):  
Gary M. Skinner ◽  
Michiel van den Hout ◽  
Onno Broekmans ◽  
Cees Dekker ◽  
Nynke H. Dekker
Keyword(s):  

ChemInform ◽  
2015 ◽  
Vol 46 (16) ◽  
pp. no-no
Author(s):  
Martin Dracinsk� ◽  
Paul Hodgkinson

2021 ◽  
Vol 118 (11) ◽  
pp. e2022806118
Author(s):  
Ke Xia ◽  
James T. Hagan ◽  
Li Fu ◽  
Brian S. Sheetz ◽  
Somdatta Bhattacharya ◽  
...  

The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.


1986 ◽  
Vol 108 (7) ◽  
pp. 1671-1675 ◽  
Author(s):  
George I. Birnbaum ◽  
Krishan L. Sadana ◽  
Wayne J. P. Blonski ◽  
Frank E. Hruska

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