Next-Generation Sequencing as Input for Chemometrics in Differential Sensing Routines
| Autor: | Camille Spears, Michelle Byrom, Alexandra M. Gade, Sara Goodwin, Baine Herrera, Andrew D. Ellington, Eric V. Anslyn |
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| Rok vydání: | 2015 |
| Předmět: |
Analyte
Multivariate statistics Principal Component Analysis Computer science Aptamer Analytical chemistry High-Throughput Nucleotide Sequencing General Chemistry Sequence Analysis DNA General Medicine Aptamers Nucleotide Catalysis DNA sequencing Plot (graphics) Article Cell Line Chemometrics Principal component analysis Multivariate Analysis Nucleic acid Humans Biological system |
| Zdroj: | Angewandte Chemie. 127:6437-6440 |
| ISSN: | 0044-8249 |
| DOI: | 10.1002/ange.201501822 |
| Popis: | Differential Sensing (DS) methods traditionally use spatially arrayed receptors and optical signals to create score plots from multivariate data that 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. Analysis of a Principal Component Analysis (PCA) 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. |
| Databáze: | OpenAIRE |
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