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Shiga toxin producing non-O157 E. coli strains such as E. coli O26 are responsible for a growing number of food-related illnesses in the United States and around the world. From food production to consumption, micro-organisms in foods experience dramatic pH fluctuations by organic acids introduced either during food processing or by inorganic acids in the stomach. Acid exposure induces specific metabolite accumulation in bacterial cells. Understanding the survival mechanisms of pathogenic micro-organisms by studying the metabolome would be helpful in introducing effective hurdles and thus ensuring food safety.

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High throughput technologies have become the practice of choice for comparative studies in biomedical applications. Limited number of sample points due to sequencing cost or access to organisms of interest necessitates the development of efficient sample collections to maximize the power of downstream statistical analyses. We propose a method for sequentially choosing training samples under the Optimal Bayesian Classification framework. Specifically designed for RNA sequencing count data, the proposed method takes advantage of efficient Gibbs sampling procedure with closed-form updates. Our results shows enhanced classification accuracy, when compared to random sampling.