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The Sequencing Bead Array (SBA), a Next-Generation Digital Suspension Array

Overview of attention for article published in PLOS ONE, October 2013
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Title
The Sequencing Bead Array (SBA), a Next-Generation Digital Suspension Array
Published in
PLOS ONE, October 2013
DOI 10.1371/journal.pone.0076696
Pubmed ID
Authors

Michael S. Akhras, Erik Pettersson, Lisa Diamond, Magnus Unemo, Jennifer Okamoto, Ronald W. Davis, Nader Pourmand

Abstract

Here we describe the novel Sequencing Bead Array (SBA), a complete assay for molecular diagnostics and typing applications. SBA is a digital suspension array using Next-Generation Sequencing (NGS), to replace conventional optical readout platforms. The technology allows for reducing the number of instruments required in a laboratory setting, where the same NGS instrument could be employed from whole-genome and targeted sequencing to SBA broad-range biomarker detection and genotyping. As proof-of-concept, a model assay was designed that could distinguish ten Human Papillomavirus (HPV) genotypes associated with cervical cancer progression. SBA was used to genotype 20 cervical tumor samples and, when compared with amplicon pyrosequencing, was able to detect two additional co-infections due to increased sensitivity. We also introduce in-house software Sphix, enabling easy accessibility and interpretation of results. The technology offers a multi-parallel, rapid, robust, and scalable system that is readily adaptable for a multitude of microarray diagnostic and typing applications, e.g. genetic signatures, single nucleotide polymorphisms (SNPs), structural variations, and immunoassays. SBA has the potential to dramatically change the way we perform probe-based applications, and allow for a smooth transition towards the technology offered by genomic sequencing.

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The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 25%
Librarian 11 21%
Student > Ph. D. Student 5 9%
Student > Master 5 9%
Professor 3 6%
Other 13 25%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 28%
Social Sciences 7 13%
Arts and Humanities 5 9%
Computer Science 3 6%
Biochemistry, Genetics and Molecular Biology 3 6%
Other 15 28%
Unknown 5 9%