We depend on antibiotics for the treatment of infectious diseases and they are critical for the success of advanced surgical procedures. Pathogenic and commensal bacteria in clinical environments are challenged with high concentrations of antibiotics and as a consequence, bacteria have become resistant to most of the antibiotics developed. Patients with infections caused by resistant pathogens often fail to respond to standard treatments, resulting in prolonged illness and a greater risk of death. In order to identify alternative treatment options, clinicians need to screen patients for resistance. However, the standard laboratory tests for resistance screening are slow, biased, and limited in their detection. The recent developments in sequencing technologies have created new opportunities to compete with the traditional tools. The large amount of data produced by high-throughput sequencing machines makes manual analysis no longer feasible and requires sophisticated analysis tools. Five computational tools are presented here to demonstrate the utility of sequence-based metagenomics for the identification of antibiotic resistance in healthy and diseased individuals. Because the identification of resistance will be used in clinical management to alter treatment regimens, the quality of the sequence data is crucial. Four novel applications were created to allow more accurate downstream analysis of sequence datasets by providing better tools for quality control and easier preprocessing methods. In addition, a computational framework was developed to create a database of antibiotic resistance mechanisms and to investigate the prevalence of resistance alleles in human microbiomes. That analysis showed significant differences between body sites and that abundant resistance genes are ubiquitous across different microbiomes. The prevalence of baseline resistance in the healthy population was shown from metagenomic data. Associations between resistance alleles and antibiotic treatment were demonstrated using data from Cystic Fibrosis patients. Resistance alleles found in metagenomic sequences were used to generate hypotheses for resistance mechanisms that were confirmed by culturing. This extensive sampling of the human microbiota across many subjects and body sites provides a characterization of the resistance potential in both healthy and diseased individuals, and the results demonstrate the potential of metagenomics for the identification and monitoring of antibiotic resistance in human microbiomes.