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Next‐Generation Sequencing as Input for Chemometrics in Differential Sensing Routines
Author(s) -
Goodwin Sara,
Gade Alexandra M.,
Byrom Michelle,
Herrera Baine,
Spears Camille,
Anslyn Eric V.,
Ellington Andrew D.
Publication year - 2015
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201501822
Subject(s) - chemometrics , analyte , aptamer , multivariate statistics , principal component analysis , biological system , nucleic acid , plot (graphics) , computational biology , computer science , pattern recognition (psychology) , curse of dimensionality , data mining , artificial intelligence , chemistry , biology , mathematics , chromatography , statistics , machine learning , genetics
Differential sensing (DS) methods traditionally use spatially arrayed receptors and optical signals to create score plots from multivariate data which classify individual analytes or complex mixtures. Herein, a new approach is described, in which nucleic acid sequences and sequence counts are used as the multivariate data without the necessity of a spatial array. To demonstrate this approach to DS, previously selected aptamers, identified from the literature, were used as semi‐specific receptors, Next‐Gen DNA sequencing was used to generate data, and cell line differentiation was the test‐bed application. The study of a principal component analysis loading plot revealed cross‐reactivity between the aptamers. The technique generates high‐dimensionality score plots, and should be applicable to any mixture of complex and subtly different analytes for which nucleic acid‐based receptors exist.