Read classification for next generation sequencing

Hogan, James M., Holland, Peter, Holloway, Alexander P., Petit, Robert A., & Read, Timothy D. (2013) Read classification for next generation sequencing. In ESANN 2013 proceedings : European Symposium on Artificial Neural Networks, Computational Intelligence, The European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 485-490.

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Next Generation Sequencing (NGS) has revolutionised molec- ular biology, allowing routine clinical sequencing. NGS data consists of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. The common bacterium Staphylococcus aureus may cause severe and life-threatening infections in humans, with some strains exhibiting antibiotic resistance. Here we apply an SVM classifier to the important problem of distinguishing S. aureus sequencing projects from other pathogens, including closely related Staphylococci. Using a sequence k-mer representation, we achieve precision and recall above 95%, implicating features with important functional associations.

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ID Code: 62013
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Next generation sequencing, Molecular biology, SVM classifier, Read classification
ISBN: 9782874190810
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Please consult the authors
Copyright Statement: You are free to download, copy and distribute any paper contained in these pages, provided that you keep the reference of the paper that has been added as header to each page.
Deposited On: 22 Aug 2013 23:10
Last Modified: 03 Feb 2014 21:56

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