MOTIVATION: High-resolution microbial strain typing is essential for various clinical purposes, including disease outbreak investigation, tracking of microbial transmission events and epidemiological surveillance of bacterial infections. The widely used approach for multilocus sequence typing (MLST) that is based on the core genome, cgMLST, has the advantage of a high level of typeability and maximal discriminatory power. Yet, the transition from a seven loci-based scheme to cgMLST involves several challenges, that include the need by some users to maintain backward compatibility, growing difficulties in the day-to-day communication within the microbiology community with respect to nomenclature and ontology, issues with typeability, especially if a more stringent approach to loci presence is used, and computational requirements concerning laboratory data management and sharing with end-users. Hence, methods for optimizing cgMLST schemes through careful reduction of the number of loci are expected to be beneficial for practical needs in different settings. RESULTS: We present a new machine learning-based methodology, minMLST, for minimizing the number of genes in cgMLST schemes by identifying subsets of informative genes and analyzing the trade-off between gene reduction and typing performance. The results achieved with minMLST over eight bacterial species show that despite the reduction in the number of genes up to a factor of 10, the typing performance remains very high and significant with an Adjusted Rand Index that ranges between 0.4 and 0.93 in different species and a P-value < 10-3. The identification of such optimized MLST schemes for bacterial strain typing is expected to improve the implementation of cgMLST by improving interlaboratory agreement and communication. AVAILABILITY AND IMPLEMENTATION: The python package minMLST is available at https://PyPi.org/project/minmlst/PyPI and supported on Linux and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep Neural Networks (DNN) can be effectively used to accurately identify infectious pathogens. Unfortunately, DNNs can be exploited by bioterrorists, using adversarial attacks, to stage a fake super-bug outbreak or to hide the extent of a super-bug outbreak. In this work, we show how a DNN that performs superb classification of cgMLST profiles can be exploited using adversarial attacks. To this end, we train a novel DNN model, Methicillin Resistance Classification Network (MRCN), which identifies strains of the Staph bacteria that are resistant to an antibiotic named methicillin with 93.8 percent accuracy, using Core Genome Multi-Locus Sequence Typing (cgMLST) profiles. To defend against this kind of exploitation, we train a second DNN model, Synthetic Profile Classifier (SPC), which can differentiate between natural Staph bacteria and generic synthetic Staph bacteria with 92.3 percent accuracy. Our experiments show that the MRCN model is highly susceptible to multiple adversarial attacks and that the defenses we propose are not able to provide effective protection against them. As a result, a bioterrorist would be able to utilize the compromised DNN model to inflict immense damage by staging a fake epidemic or delaying the detection of an epidemic, allowing it to proliferate undeterred.