Disease-associated changes to the gastrointestinal (GI) microbiome have been detected in a variety of chronic human illnesses. Currently, GI microorganisms are thought to play a major role in systemic health and homeostasis through interactions with the immune system of their host. In recent years, the available technology for capturing and characterizing the GI microbiome has expanded greatly, resulting in a rapid expansion of the field. In particular, culture-independent technologies allowing for direct analysis of microbial genes, transcripts, proteins, and other metabolites (collectively referred to as meta-omics) from stool samples show potential for personalized disease detection and monitoring through non-invasive screening. However, due to the high degree of variability inherent in microbiome profiles, establishing a consensus for microbial signatures or biomarkers of disease across studies is difficult. Differences in study methodology can further complicate comparisons, since the laboratory protocols for sequence-based data capture and analysis are varied, and there are several commercial kits and reagents available for the storage and isolation of microbial DNA and RNA from stool for downstream microbiome profiling. Research has shown that the choice of nucleic acid extraction kit and storage method can affect resulting nucleic acid quality. In the current methodological study, a single stool sample from a healthy donor is divided and processed using multiple commercially available methods for nucleic acid stabilization and isolation in order to evaluate the effect of processing on observed sequence-based microbiome profiles. The research herein demonstrates that the experimental methodology used to stabilize and isolate nucleic acids from human stool samples can significantly impact the ability to capture GI microbiome diversity from metagenomic and metatranscriptomic data. Notably, GI microbiome characteristics commonly used as health markers in disease research are differentially impacted by the specific combination of preservative reagent and nucleic acid extraction kit. This study additionally describes important considerations for future meta-omic microbiome research, including the choice of bacterial lysis approach, bioinformatic analysis methodology, and potentially detrimental vendor mismatching. Ultimately, the current research supports the use of informed experimental design and expands current understanding of how biological sample integrity and experimental bias may affect observed meta-omic microbiome profiles.