Working with any plant-associated microbe comes with the inherent challenge that no environment is sterile, and a plant’s metabiome is teeming with life. When collecting field samples outside of the laboratory, this issue is compounded further. Rust fungi, being obligate plant pathogens, are challenging to maintain, and strict protocols must be adhered to in the laboratory to prevent cross-contamination. In this era of big data and easy access to next generation sequencing (NGS), it is increasingly common for scientists to work with large sequencing datasets, which must first be evaluated for quality and filtered for potentially nontarget reads. Sequencing data files from environmental samples often contain genetic material from organisms not targeted by the experimental design. This situation can lead to issues if researchers assume the presence of only their intended subjects. Additionally, the origin of some samples may be inherently unknown, making the main objective of certain sequencing experiments to identify all organisms present, not just the expected ones. This chapter details common in silico approaches for identifying and classifying samples from sequencing data, drawing on experiences with cereal rust samples collected in the field. While the concepts are broadly applicable, they may require some tailoring for your species of interest. The chapter does not cover population-genomics level approaches to sample classification; instead, it describes essential quality control steps that researchers should implement before conducting downstream analyses to ensure that their data is appropriate for the intended tests.