Currently, assessment of the efficacy of vaccination against tuberculosis in laboratory animal models relies on the challenge of animals with the pathogen and subsequent post mortem examination to quantify pathology or bacterial burden. Similarly, the assessment of vaccine efficacy in a number of other infectious disease models where no reliable correlate of protection is available requires disease challenge. In some cases, such studies may result in suffering in non-protected animals. This is not the case, however, for those diseases where successful vaccination can be reliably predicted on the basis of, for example, a defined antibody response. The development of next-generation sequencing technologies, which allow the measurement of global gene expression profiles, and their comparison between different experimental treatments, has great potential for the identification of reliable correlates of protection. We propose a pilot study to apply a transcriptome sequencing approach to identify correlates of protection from disease challenge, using samples generated during BCG vaccine efficacy studies in badgers (which act as a wildlife reservoir of bovine tuberculosis infection in the UK). This has significant potential to ultimately reduce the number of laboratory animals submitted to disease challenge in vaccine research studies, in addition to increasing the amount of information obtained from such studies. Additionally, this work has the potential to result in refinement of vaccine efficacy experiments, through removal of the need to submit animals to more severe procedures and removal of the requirement for housing in containment facilities.