We describe IsoBase, a database identifying functionally related proteins, across five major eukaryotic model organisms: Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus and Homo Sapiens. Nearly all existing algorithms for orthology detection are based on sequence comparison. Although these have been successful in orthology prediction to some extent, we seek to go beyond these methods by the integration of sequence data and protein– protein interaction (PPI) networks to help in identifying true functionally related proteins. With that motivation, we introduce IsoBase, the first publicly available ortholog database that focuses on functionally related proteins. The groupings were computed using the IsoRankN algorithm that uses spectral methods to combine sequence and PPI data and produce clusters of functionally related proteins. These clusters compare favorably with those from existing approaches: proteins within an IsoBase cluster are more lik...