OBJECTIVES/GOALS: The goal of this study is to reveal strong candidate genes for inherited retinal diseases (IRDs) in humans to better understand the mechanisms behind IRD development and reveal potential therapeutic targets. We hope these findings will help improve our understanding of IRDs and, subsequently, diagnostic accuracy and prognosis for IRD patients. METHODS/STUDY POPULATION: The goal of the International Mouse Phenotyping Consortium is to identify the function of all protein-coding genes in the mouse genome via generation and phenotypic characterization of single gene knockout (KO) mice. Using this database, we identified all KO strains associated with abnormal retinal phenotypes, removed all RNA coding genes and pseudogenes, converted to human orthologues, and conducted a literature search for existing research regarding candidate IRD genes and retinal function/abnormalities. A similar process was used for RetNet genes. Subsequently, we performed bioinformatics analysis, including functional annotation (e.g. panther-db), pathway analysis (e.g. KEGG), and string-db to visualize known and predicted protein-protein interactions between the two data sets. RESULTS/ANTICIPATED RESULTS: Analysis of the IMPC database revealed, out of 8481 phenotyped genes, 572 unique protein coding genes were associated with 14 categories of retinal abnormalities such as abnormal retinal vasculature and abnormal retinal thickness, 377 of which have never been associated with retinal pathology in humans or mice. Pathway analysis of the IMPC database highlighted a general metabolism pathway as well as PI3K-Akt and MAPK pathways, not found in RetNet pathway results. Unique clusters from functional annotation clustering of the IMPC include DNA methylation and protein ubiquination. Visualization of protein-protein interactions in string-db between the IMPC (mouse) and RetNet (human) revealed 4 clusters of interest with gold standard RetNet IRD proteins interacting with candidate IMPC IRD proteins. DISCUSSION/SIGNIFICANCE: IMPC analysis revealed 572 candidate IRD genes, 377 of which are novel with no existing independent research related to the retina outside of the IMPC. Bioinformatic analysis reveals 4 strong clusters of interest through string-db where a gold standard RetNet gene interacts with candidate IRD genes as well as many functional pathways of interest.