Over the past few years, microbiome studies have broadened our understanding of the interactions between microbial communities and their animal and plant hosts. The sequencing technology underlying these studies is powerful and sensitive. Based on the detection of contaminants in genomic DNA prep kits, it is now strongly recommended to include negative kit controls alongside samples in microbiome studies. Although the importance of negative controls is undisputed, questions remain regarding how these data should be used to process and interpret sample data. A conventional method used in microbiome research is to remove Operational Taxonomic Units (OTUs) from the sample data if they are detected in negative controls. This method assumes that all sequences found in negative controls arise from contamination in the kit itself. However, it is possible that when controls are processed alongside samples, sample material contaminates the negative controls. If this is true, eliminating OTUs from datasets due to detection in the negative kit control would be an overly conservative approach. To explore this, we sequenced and analyzed negative controls processed alone and with samples of three different types. This is a unique opportunity to look at the impact of sample type on the negative kit control that was processed with it and address the question of how much of what is found in a negative kit control can be attributed to the kit itself and how much can be ascribed to the samples they were prepped alongside. To do this, we sequenced and analyzed the V4 region of the 16S rRNA gene using the open source software package mothur. First, we examined negative kit controls processed on their own (no samples processed alongside). We compared three preps from a kit that had been previously opened and handled during sample processing and three preps from an unopened kit to analyze variation among preps from a single kit and identify contamination that may occur due to normal handling of the kit. We found no significant difference between the negative controls from the opened and unopened kits. Second, we compared negative kit controls processed on their own to controls processed alongside either monkey fecal samples, rat fecal samples or samples from a composting toilet. We found a higher number of sequences in the controls processed alongside samples, and we are currently conducting statistical analyses on these data. Finally, we will compare the data from all negative kit controls and the samples themselves. We predict that the OTU profiles of kit controls processed alongside samples will be more similar to the sample type that they were processed alongside. We also expect that the negative kit controls processed with samples will be significantly different from those processed alone. This study will provide information on how negative control data should be processed and interpreted in microbiome studies, which is an important open question in the field today.