title: Supervised learning for the automated transcription of spacer classification from spoligotype films.
abstract: BACKGROUND
  Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing.
  
  
  RESULTS
  Features extracted from each of the 1849 spots on a spoligo film were classified using two supervised learning algorithms. A graphical user interface allows manual editing of the classification, before export to a database. The application was tested on ten films of differing quality and the results of the best classifier were compared to expert manual classification, giving a median correct classification rate of 98.1% (inter quartile range: 97.1% to 99.2%), with an automated processing time of less than 1 minute per film.
  
  
  CONCLUSIONS
  The software implementation offers considerable time savings over manual processing whilst allowing expert editing of the automated classification. The automatic upload of the classification to a database reduces the chances of transcription errors.
authors.affiliation: MRC Laboratories, Fajara, The Gambia. djeffries@mrc.gm
authors.forename: David J
authors.initials: DJ
authors.lastname: Jeffries
authors.forename: Neil
authors.initials: N
authors.lastname: Abernethy
authors.forename: Bouke C
authors.initials: BC
authors.lastname: de Jong
mesh_terms: D000465:Algorithms; D015373:Bacterial Typing Techniques; D005714:Gambia; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000145:classification* / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D014376:Tuberculosis / Q000175:diagnosis
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