Fast subcellular localization by cascaded fusion of signal-based and homology-based methods
Fast subcellular localization by cascaded fusion of signal-based and homology-based methods
Blog Article
Abstract Background The functions of proteins are closely related to their subcellular locations.In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.Results This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment.
Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals.Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles.Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier.
To further reduce the training and recognition time of mel axolotl the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).Conclusions Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences.
Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy.It was found that PDA enjoys a short training chorulon hcg time as compared to the conventional SVM.We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.