Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes |
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| Institution: | 1. Department of Medicine, Adult Bone Marrow Transplantation Service, Memorial Sloan Kettering Cancer Center, New York, New York;2. PhD Program in Signals Integration and Modulation in Biomedicine, Cell therapy and Translational Medicine, University of Murcia, Murcia, Spain;3. Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, New York;4. University of Connecticut Medical Center, Farmington, Connecticut;5. Department of Medicine, Lymphoma Service, Memorial Sloan Kettering Cancer Center, New York, New York;6. Weill Cornell Medical College, New York, New York;7. Department of Pediatrics, Stem Cell Transplantation and Cellular Therapies Program, Memorial Sloan Kettering Cancer Center, New York, New York;8. Department of Pediatric Hematology, Oncology and Cellular Therapy, Children''s Hospital at Montefiore, Bronx, New York;9. Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York;1. Research Scholar, Reg. No. 20111152132002, PG and Research Department of Physics, Pope''s College, Affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India;2. PG and Research Department of Physics, Pope''s College, Affiliated to ManonmaniamSundaranar University, Tirunelveli, Tamil Nadu 627012, India;3. Department of Physics, Aditanar College of Arts and Science, Affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, India |
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| Abstract: | Signal peptides play a crucial role in various biological processes, such as localization of cell surface receptors, translocation of secreted proteins and cell–cell communication. However, the amino acid mutation in signal peptides, also called non-synonymous single nucleotide polymorphisms (nsSNPs or SAPs) may lead to the loss of their functions. In the present study, a computational method was proposed for predicting deleterious nsSNPs in signal peptides based on random forest (RF) by incorporating position specific scoring matrix (PSSM) profile, SignalP score and physicochemical properties. These features were optimized by the maximum relevance minimum redundancy (mRMR) method. Then, a cost matrix was used to minimize the effect of the imbalanced data classification problem that usually occurred in nsSNPs prediction. The method achieved an overall accuracy of 84.5% and the area under the ROC curve (AUC) of 0.822 by Jackknife test, when the optimal subset included 10 features. Furthermore, on the same dataset, we compared our predictor with other existing methods, including R-score-based method and D-score-based methods, and the result of our method was superior to those of the two methods. The satisfactory performance suggests that our method is effective in predicting the deleterious nsSNPs in signal peptides. |
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