SubChlo-GO: Predicting Protein Subchloroplast Locations with Weighted Gene Ontology Scores

Pufeng      Du; Tingting      Li; Xin      Wang; Chao      Xu

doi:10.2174/1574893611308020007

Abstract

Chloroplasts are subcellular organelles found only in green plants and eukaryotic algae. Chloroplasts are of central importance in the photosynthesis process. The subchloroplast localizations of chloroplast proteins are critical in understanding their functions and important for fully decipher the photosynthesis process. Although there are several existing methods that computationally determine protein subchloroplast localizations, prediction performance and software availability can still be improved. We proposed a novel computational method, namely, the Weighted Gene Ontology Scores, to predict protein subchloroplast locations. This method can achieve at least 88% prediction accuracy on the benchmarking dataset, which is significantly higher than existing methods. SubChlo-GO, which is an easy-to-use webbased online service, has been constructed based on the proposed method. We hope that SubChlo-GO could be helpful in chloroplast proteome research.

Keywords: Accuracy deviation, gene ontology, gene ontology supporting set, SubChlo-GO, subchloroplast locations, weighted gene ontology scores, Stroma, Thylakoid lumen, Thylakoid membrane, Envelope,