Comparative pathway prediction via unified graph modeling of genomic structure information

Genomic information other than sequence similarity is important for comparative analysis based prediction of biological pathways. There is evidence that structure information like protein-DNA interactions and operons is very useful in improving the pathway prediction accuracy. This paper introduces a graph model that can unify the protein-DNA interaction and operon information as well as homologous relationships between involved genes. Under this model, pathway prediction corresponds to finding the maximum independent set in the model graph, which is solved efficiently via non-trivial tree decomposition-based techniques. The developed algorithm is evaluated based on the prediction of 30 pathways in E. coli K12 using those in B. subtilis 168 as templates. The overall accuracy of the new method outperforms those based solely on sequence similarity or using different categories of structure information separately.