  Rapidly assaying the diversity of a bacterial species present in a sample
obtained from a hospital patient or an evironmental source has become possible
after recent technological advances in DNA sequencing. For several applications
it is important to accurately identify the presence and estimate relative
abundances of the target organisms from short sequence reads obtained from a
sample. This task is particularly challenging when the set of interest includes
very closely related organisms, such as different strains of pathogenic
bacteria, which can vary considerably in terms of virulence, resistance and
spread. Using advanced Bayesian statistical modelling and computation
techniques we introduce a novel pipeline for bacterial identification that is
shown to outperform the currently leading pipeline for this purpose. Our
approach enables fast and accurate sequence-based identification of bacterial
strains while using only modest computational resources. Hence it provides a
useful tool for a wide spectrum of applications, including rapid clinical
diagnostics to distinguish among closely related strains causing nosocomial
infections. The software implementation is available at
https://github.com/PROBIC/BIB
