Ticks and the pathogens they transmit constitute a growing burden for human and animal health worldwide. Traditionally, tick-borne pathogen detection has been carried out using PCR-based methods that rely in known sequences for specific primers design. This approach matches with the view of a ‘single-pathogen’ epidemiology. Recent results, however, have stressed the importance of coinfections in pathogen ecology and evolution with impact in pathogen transmission and disease severity. New approaches, including high-throughput technologies, were then used to detect multiple pathogens, but they all need a priori information on the pathogens to search. Thus, those approaches are biased, limited and conceal the complexity of pathogen ecology. Currently, next generation sequencing (NGS) is applied to tick-borne pathogen detection as well as to study the interactions between pathogenic and non-pathogenic microorganisms associated to ticks, the pathobiome. The use of NGS technologies have surfaced two major points: (i) ticks are associated to complex microbial communities and (ii) the relation between pathogens and microbiota is bidirectional. Notably, a new challenge emerges from NGS experiments, data analysis. Discovering associations among a high number of microorganisms is not trivial and therefore most current NGS studies report lists of microorganisms without further insights. An alternative to this is the combination of NGS with analytical tools such as network analysis to unravel the structure of microbial communities associated to ticks in different ecosystems.