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http://hdl.handle.net/10397/6254
| Title: | Fast subcellular localization by cascaded fusion of signal-based and homology-based methods | Authors: | Mak, MW Wang, W Kung, SY |
Issue Date: | 14-Oct-2011 | Source: | Proteome science, 14 Oct. 2011, v. 9, suppl. 1, S8, p.1-12 | 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 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 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. |
Publisher: | BioMed Central | Journal: | Proteome science | EISSN: | 1477-5956 | DOI: | 10.1186/1477-5956-9-S1-S8 | Rights: | © 2011 Mak et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Appears in Collections: | Journal/Magazine Article |
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| Mak_Subcellular_Localization_Signal-based.pdf | 1.04 MB | Adobe PDF | View/Open |
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