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DOI	10.1073/pnas.2022806118
	Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis
	Xia K.; Hagan J.T.; Fu L.; Sheetz B.S.; Bhattacharya S.; Zhang F.; Dwyer J.R.; Linhardt R.J.
发表日期	2021
ISSN	00278424
卷号	118 期号:11
英文摘要	The application of solid-state (SS) nanopore devices to singlemolecule 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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Glycosaminoglycan; Polysaccharide; Sequencing; Single-molecule analysis; Solid-state nanopore
语种	英语
scopus关键词	disaccharide; glycosaminoglycan; heparan sulfate; monosaccharide; algorithm; analytic method; Article; carbohydrate analysis; chemical composition; machine learning; priority journal; reaction duration (chemistry); signal transduction; solid state nanopore analysis
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180290
作者单位	Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, United States; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, United States; Department of Chemistry, University of Rhode Island, Kingston, RI 02881, United States; Howard P. Isermann Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States
推荐引用方式 GB/T 7714	Xia K.,Hagan J.T.,Fu L.,et al. Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis[J],2021,118(11).
APA	Xia K..,Hagan J.T..,Fu L..,Sheetz B.S..,Bhattacharya S..,...&Linhardt R.J..(2021).Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis.Proceedings of the National Academy of Sciences of the United States of America,118(11).
MLA	Xia K.,et al."Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis".Proceedings of the National Academy of Sciences of the United States of America 118.11(2021).