Open Access Methodology article

A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences

Fredrik Boulund1, Anna Johnning2, Mariana Buongermino Pereira1, DG Joakim Larsson3 and Erik Kristiansson1*

Author Affiliations

1 Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Göteborg, SE-412 96, Sweden

2 Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Box 434, Göteborg, SE-405 30, Sweden

3 Department of Infectious Diseases, Institute of Biomedicine, the Sahlgrenska Academy at the University of Gothenburg, Box 434, Göteborg, SE-405 30, Sweden

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BMC Genomics 2012, 13:695  doi:10.1186/1471-2164-13-695

Published: 11 December 2012

Additional files

Additional file 1:

Figure S1. Fragment bit scores with HMM constructed without QnrA. Bit scores of fragments against the hidden Markov model where all sequences from QnrA were excluded.

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Additional file 2:

Figure S2. Fragment bit scores with HMM constructed without QnrB. Bit scores of fragments against the hidden Markov model where all sequences from QnrB were excluded.

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Additional file 3:

Figure S3. Fragment bit scores with HMM constructed without QnrC. Bit scores of fragments against the hidden Markov model where all sequences from QnrC were excluded.

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Additional file 4:

Figure S4. Fragment bit scores with HMM constructed without QnrD. Bit scores of fragments against the hidden Markov model where all sequences from QnrD were excluded.

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Additional file 5:

Figure S5. Fragment bit scores with HMM constructed without QnrS. Bit scores of fragments against the hidden Markov model where all sequences from QnrS were excluded.

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Additional file 6:

Figure S6. Specificity. The specificity in classification of fragments of novel qnr genes for each of the five models. The line QnrA denotes the specificity of the model constructed without QnrA to accurately classify fragments from QnrA. The same for QnrB, C, D and S.

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Additional file 7:

Table S1. Annotation of the 475 groups of sequences discovered in this work.

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Additional file 8:

Figure S7. Overview of the pipeline implementation. A flowchart describing the major parts of the pipeline implemented in Python.

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