ABSTRACT
Developed proteome-scale ortholog and paralog prediction methods are mainly based on sequence similarity. However, it is known that even the closest BLAST hit often does not mean the closest neighbor. For this reason, we added conserved interaction information to find orthologs. We propose a genome-scale, automated ortholog prediction method, named OrthoInterBlast. The method is based on both sequence and interaction similarity. When we applied this method to fly and yeast, 17% of the ortholog candidates were different compared with the results of Inparanoid. By adding protein-protein interaction information, proteins that have low sequence similarity still can be selected as orthologs, which can not be easily detected by sequence homology alone.
Subject(s)
Diptera , Proteins , Sequence Homology , YeastsABSTRACT
Fusion proteins resulting from chimeric sequences are excellent targets for therapeutic drug development. We developed a database of chimeric sequences by examining the genomic alignment of mRNA and EST sequences in the GenBank. We identified 688 chimeric mRNA and 20,998 chimeric EST sequences. Including EST sequences greatly expands the scope of chimeric sequences even though it inevitably accompanies many artifacts. Chimeric sequences are clustered according to the ECgene ID so that the user can easily find chimeric sequences related to a specific gene. Alignments of chimeric sequences are displayed as custom tracks in the UCSC genome browser. ChimerDB, available at http://genome.ewha.ac.kr/ECgene/ChimerDB/, should be a valuable resource for finding drug targets to treat cancers.