Neisseria meningitidis is a gram negative, and sometimes encapsulated, diplococcus that causes devastating disease worldwide. For the worldwide genetic surveillance of N. meningitidis, the gold standard for profiling the bacterium uses genetic loci found around the genome. Unfortunately, the software for analyzing the data for these profiles is difficult to use for a variety of reasons. This thesis shows my suite of tools called the Meningococcus Genome Informatics Platform for the analysis of these profiling data. To better understand N. meningitidis, the CDC Meningitis Laboratory and other world class laboratories have adopted a whole genome approach. To facilitate this approach, I have developed a computational genomics assembly and annotation pipeline called the CG-Pipeline. It assembles a genome, predicts locations of various features, and then annotates those features. Next, I developed a comparative genomics browser and database called NBase. Using CG-Pipeline and NBase, I addressed two open questions in N. meningitidis research. First, there are N. meningitidis isolates that cause disease but many that do not cause disease. What is the genomic basis of disease associated versus asymptomatically carried isolates of N. meningitidis? Second, some isolates' capsule type cannot be easily determined. Since isolates are grouped into one of many serogroups based on this capsule, which aids in epidemiological studies and public health response to N. meningitidis, often an isolate cannot be grouped. Thus the question is what is the genomic basis of nongroupability? This thesis addresses both of these questions on a whole genome level.