IntroductionBioinformatics-based tools are essential for designing experiments in the post-genomic era. They allow scientists to manipulate the vast data sets resulting from genome sequencing efforts to identify potential research targets; analyze target sequences to predict protein characteristics; and share annotated data via 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 tool limitations, parameter adjustments, and biological considerations to ensure a reliable hypothesis when using bioinformatics. Additionally, a strong fundamental knowledge of these techniques will increase their accuracy and efficiency, leading to better initial experiments. An important biological consideration that dictates which bioinformatics tools to use is whether the sequence data comes from a prokaryotic or eukaryotic organism. Many tools will have options to select the classification your footage comes from and some will only work with a certain classification. Indeed, there are major 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 analyze and the insights you hope to extract. This creates two analysis steps that take place during experimental design using bioinformatics tools. These are the search for genes and the prediction of gene function; together, they can identify potential targets for research and spark imp...... middle of article ......d to account for these differences when identifying genes and predicting of their function. Prokaryotic genomes also possess synteny, making comparative genomics a useful tool for identifying small genes that would be overlooked in more stringent gene search tools such as ORF analysis. Gene function prediction involves predicting protein localization and defining conserved functional domains. These both depend on whether the target sequence is of prokaryotic or eukaryotic origin, as different signaling peptides, localization possibilities and useful domains exist for each classification. However, gene expression data has been neglected as a method of functional analysis, because the analysis of both classifications follows a similar method. Gene expression data are useful because they further reduce the ambiguity of protein function at specific cellular events..