With the principle of "compare and contrast," comparative genomics studies multiple genomes to look for essential genes that sustain life of an organism. For pathogenic organisms, comparative genomics enables identification of target genes that are common among specific pathogens and are likely to be important in allowing these pathogens to cause disease. It has also become possible to infer protein function by comparison of multiple genomic sequences in an evolutionary context. This is accomplished by searching for proteins with conserved function across genomes (orthologs) and by assigning function to a whole ortholog set if there are members with known function in the set. New computational algorithms and high performance computing are needed to perform these analyses and accommodate the rapid increase of available genomic data. We initiated comparative tools development by implementing an algorithm to predict orthologous proteins across multiple genomes. This will provide additional information for function annotation not available from a single genome homology search using PIPA or other tools. Another type of analysis of genomic sequences involves the usage of triplets of nucleotides that encode each amino acid (codon usage bias) in selected genes. A large codon usage bias for a particular set of genes with respect to the average usage in the whole genome is considered as an indication that these genes are highly expressed or encode important functions for the organism. We are implementing algorithms to evaluate such codon usage statistics for each genome of interest and for comparison between genomes. In the near future, we will implement multiple genome alignment and phylogenetic analysis tools. We are also developing a GUI to support these newly developed comparative tools.