The genes in NCBI databases are currently annotated with itemized text (Gene Reference Into Function, or GeneRIF). A previous work suggests that the visual presentation can be more effective when time and space are under heavy constraints. In this thesis we first report a novel annotation of the genome information using Web 2.0 technologies: GeneGIF (Gene Graphics Into Function). The users can quickly scan through important functions of each gene from a graph, and then go to detailed pages when they find interesting annotations. The modular implementation makes it easily pluggable into other widely used databases without reprogramming. Then we present another web based tool - ListGIF which derives over represented concepts for a list of genes from biomedical literature. ListGIF supports two literature resources: GeneRIF and Gene Ontology. Our strategy is based on the idea that the significance of a feature is associated with the number of literature co-occurrences among each gene's annotation in the list. To reduce the bias that unbalanced GeneRIF distribution among genes might bring to the result, we provide both gene level and GeneRIF level analysis. Result is also presented in a word-cloud like graph with traceability to original published evidence
