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A practical introduction to microbial molecular ecology through the use of isolation chips.

ABSTRACT: In the context of antimicrobial resistance as one of the most serious issues faced globally by health providers, we explored a practical introduction to molecular microbial ecology. We designed field work and practical experiments for third year members of a 4 year undergraduate Masters Program, in which the students employed traditional and novel isolation techniques to identify antimicrobial activities from soil dwelling microorganisms. Students gained experience in isolating DNA from complex microbial communities, amplifying 16S rRNA genes and applied richness/diversity indices as well as principal coordinate analyses to the interpretation of the data they obtained from high throughput sequencing. Our results confirmed that isolation chips facilitate the growth of a greater diversity and different species subset from the complex soil microorganism community than traditional plate spreading techniques. However, rarefaction of 16S rRNA amplicon sequencing data showed that the majority of observed species in soil remain unculturable by current methods. Based on the written reports produced by the students carrying out the work, we concluded that the described protocols are robust and informative, that these activities provide a good practical introduction to the theories and practice of molecular ecology and can be easily deployed to groups of six or more students in a cost-effective manner.

SUBMITTER: Alessi AM

PROVIDER: S-EPMC6309002 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A practical introduction to microbial molecular ecology through the use of isolation chips.

Alessi Anna M AM Redeker Kelly R KR Chong James P J JPJ

Ecology and evolution 20181211 24

In the context of antimicrobial resistance as one of the most serious issues faced globally by health providers, we explored a practical introduction to molecular microbial ecology. We designed field work and practical experiments for third year members of a 4 year undergraduate Masters Program, in which the students employed traditional and novel isolation techniques to identify antimicrobial activities from soil dwelling microorganisms. Students gained experience in isolating DNA from complex ...[more]

PMID: 30619545

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Project description:BackgroundFollowing visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data.MethodsWe demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment.ResultsThe trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble.ConclusionsWe use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.

Dataset Information

A practical introduction to microbial molecular ecology through the use of isolation chips.

Publications

A practical introduction to microbial molecular ecology through the use of isolation chips.

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