The diversity and dynamics of microorganisms in engineered ecosystems have a high impact on performance and operational stability. Nitrogen removal from wastewater is one example of such complex and dynamic ecosystems. Following the microbial community composition and its functional potential is highly valuable information for optimizing performance. Molecular methods and data analysis tools have become more and more popular in recent years. PICRUSt, a bioinformatics tool to predict the functional potential of a sample from 16S rRNA amplicon sequencing, was tested in the context of autotrophic nitrogen removal for its accuracy. Two experimental studies were extended by qPCR to demonstrate how qPCR can be used to deliver information about the accuracy of PICRUSt predictions. Two main points were discovered: (1) the correlation between qPCR data and PICRUSt predictions depends on the relative abundance of the target gene. With higher abundance, better correlations are achievable; (2) the more genome information available, the stronger the correlation.