Supervised learning for the automated transcription of spacer classification from spoligotype filmsReport as inadecuate




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BMC Bioinformatics

, 10:248

First Online: 12 August 2009Received: 21 May 2009Accepted: 12 August 2009

Abstract

BackgroundMolecular 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.

ResultsFeatures 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.

ConclusionThe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-10-248 contains supplementary material, which is available to authorized users.

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Author: David J Jeffries - Neil Abernethy - Bouke C de Jong

Source: https://link.springer.com/







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