IntroductionBioinformatics-based tools are essential for designing experiments in the post-genomics era. They allow scientists to manipulate large datasets obtained from genome sequencing efforts to identify potential research targets; analyze target sequences to predict protein characteristics; and share annotated data through simple on-demand interfaces. This gives researchers more information to use when creating a hypothesis, saving time and money that would have been spent on failed experiments. Informed use of these tools is necessary to avoid false positive and negative results. This requires knowledge of instrument limitations, parameter adjustments, and biological considerations to ensure a confident hypothesis when using bioinformatics. Furthermore, a strong fundamental understanding of these techniques will increase their accuracy and efficiency, leading to better initial experiments. An important biological consideration that determines which bioinformatic tools should be used is whether the sequence data is taken from a prokaryotic or eukaryotic organism. Many tools will have options to select which classification the sequence comes from, and some will only work with a certain classification. This is because there are large differences in the organization and processing of genetic information between prokaryotes and eukaryotes. However, only some differences between the two classifications are important; depending on the data you are analyzing and the information you hope to extract. This creates two phases of analysis that take place during experimental design using bioinformatic tools. It involves discovering genes and predicting gene function; together, they can identify potential targets for research and spark the impetus...... middle of paper ......to consider these differences when identifying genes and predicting their function. Prokaryotic genomes also possess syntheses, making comparative genomics a useful tool for identifying small genes that would be overlooked in more rigorous genetic research tools such as ORF scanning. Gene function prediction revolves around predicting protein localization and defining conserved functional domains. Both depend on whether the target sequence is of prokaryotic or eukaryotic origin, as for each classification there are different signaling peptides, localization possibilities, and useful domains. However, gene expression data has been overlooked as a functional analysis method since the analysis of both classifications follows a similar method. Gene expression data are useful because they further narrow the ambiguity of protein function to specific cellular events.