Read classification for next generation sequencing
Abstract
Next Generation Sequencing (NGS) has revolutionised molecular 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.
Citation
Hogan, J. M., Holland, P., Holloway, A. P., Petit, R. A., III & Read, T. D. Read Classification for Next Generation Sequencing. ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence (2013)