Protein (multi-)location prediction: Using location inter-dependencies in a probabilistic framework

Algorithms for Molecular Biology

Volumn 9, Issue 1, 2014, Pages

  Simha, Ramanuja ^a   Shatkay, Hagit ^a,b,c  

^a University of Delaware   (United States)

^b QUEEN S UNIVERSITY   (Canada)

^c UNIVERSITY OF DELAWARE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84899529206     PISSN: None     EISSN: 17487188     Source Type: Journal    
DOI: 10.1186/1748-7188-9-8     Document Type: Article

References (56)

1. Bakheet T, Doig A. Properties and identification of human protein drug targets. Bioinformatics 2009, 25(4):451-457. 10.1093/bioinformatics/btp002, 19164304.
2. Dreger M. Proteome analysis at the level of subcellular structures. Eur J Biochem 2003, 270(4):589-99. 10.1046/j.1432-1033.2003.03426.x, 12581199.
3. Hanson M, Kohler R. GFP imaging: methodology and application to investigate cellular compartmentation in plants. J Exp Bot 2001, 52(356):529-539. 10.1093/jexbot/52.356.529, 11373302.
4. Nakai K, Kanehisa M. Expert system for predicting protein localization sites in gram-negative bacteria. Proteins 1991, 11(2):95-110. 10.1002/prot.340110203, 1946347.
5. Emanuelsson O, Nielsen H, Brunak S, von Heijne G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 2000, 300(4):1005-1016. 10.1006/jmbi.2000.3903, 10891285.
6. Rey S, Gardy J, Brinkman F. Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria. BMC Genomics 2005, 6:162. 10.1186/1471-2164-6-162, 1314894, 16288665.
7. Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, Kohlbacher O. SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data. Bioinformatics 2007, 23(11):1410-1417. 10.1093/bioinformatics/btm115, 17392328.
8. Blum T, Briesemeister S, Kohlbacher O. MultiLoc2: integrating phylogeny and gene ontology terms improves subcellular protein localization prediction. BMC Bioinformatics 2009, 10:274. 10.1186/1471-2105-10-274, 2745392, 19723330.
9. Bairoch A, Apweiler R, Wu C, Barker W, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin M, Natale D, O'Donovan C, Redaschi N, Yeh L. The Universal Protein Resource (UniProt). Nucleic Acids Res 2005, 33(suppl 1):154-159.
10. Foster L, de Hoog C, Zhang Y, Zhang Y, Xie X, Mootha V, Mann M. A mammalian organelle map by protein correlation profiling. Cell 2006, 125(1):187-199. 10.1016/j.cell.2006.03.022, 16615899.
11. Zhang S, Xia X, Shen J, Zhou Y, Sun Z. DBMLoc: a database of proteins with multiple subcellular localizations. BMC Bioinformatics 2008, 9:127. 10.1186/1471-2105-9-127, 2292141, 18304364.
12. Millar A, Carrie C, Pogson B, Whelan J. Exploring the function-location nexus: using multiple lines of evidence in defining the subcellular location of plant proteins. Plant Cell 2009, 21(6):1625-1631. 10.1105/tpc.109.066019, 2714922, 19561168.
13. Murphy R. Communicating subcellular distributions. Cytometry A 2010, 77(7):686-92. 2901539, 20552685.
14. Pohlschroder M, Hartmann E, Hand N, Dilks K, Haddad A. Diversity and evolution of protein translocation. Annu Rev Microbiol 2005, 59:91-111. 10.1146/annurev.micro.59.030804.121353, 16153164.
15. Rea S, James D. Moving GLUT4: The biogenesis and trafficking of GLUT4 storage vesicles. Diabetes 1997, 46(11):1667-1677. 10.2337/diab.46.11.1667, 9356011.
16. Russell R, Bergeron R, Shulman G, Young H. Translocation of myocardial GLUT-4 and increased glucose uptake through activation of AMPK by AICAR. Am J Physiol 1997, 277:643-649.
17. King B, Guda C. ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes. Genome Biol 2007, 8(5):68.
18. Russell S, Norvig P. Artificial Intelligence - A Modern Approach 2010, New Jersey, USA: Pearson Education.
19. Li L, Zhang Y, Zou L, Zhou Y, Zheng X. Prediction of protein subcellular multi-localization based on the general form of Chou's pseudo amino acid composition. Protein Pept Lett 2012, 19(4):375-387. 10.2174/092986612799789369, 22185507.
20. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques 2011, San Francisco, USA: Morgan Kaufmann Publishers Inc.
21. Scholkopf B, Smola A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Massachusetts 2002, USA: MIT Press.
22. Horton P, Obayashi T, Nakai K. Protein subcellular localization prediction with WoLF PSORT. Asian Pacific Bioinformatics Conference, 2006. Proceedings 2006, 39-48. London, UK: Imperial College Press.
23. Horton P, Park K, Obayashi T, Fujita N, Harada H, Adams-Collier C, Nakai K. WoLF PSORT: protein localization predictor. Nucleic Acids Res 2007, 35(Web Server issue):585-587.
24. Chou K, Shen H. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 2007, 6(5):1728-1734. 10.1021/pr060635i, 17397210.
25. Chou K, Wu Z, Xiao X. iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS ONE 2011, 6(3):18258.
26. Briesemeister S, Rahnenfuhrer J, Kohlbacher O. Going from where to why - interpretable prediction of protein subcellular localization. Bioinformatics 2010, 26(9):1232-1238. 10.1093/bioinformatics/btq115, 2859129, 20299325.
27. Chou K, Wu Z, Xiao X. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol BioSyst 2012, 8(2):629-641. 10.1039/c1mb05420a, 22134333.
28. Wu Z, Xiao X, Chou K. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol BioSyst 2011, 7(12):3287-3297. 10.1039/c1mb05232b, 21984117.
29. Xiao X, Wu Z, Chou K. iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 2011, 284(1):42-51. 10.1016/j.jtbi.2011.06.005, 21684290.
30. Xiao X, Wu Z, Chou K. A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS ONE 2011, 6(6):20592.
31. Wu Z, Xiao X, Chou K. iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins. Protein Pept Lett 2012, 19(1):4-14. 10.2174/092986612798472839, 21919865.
32. Chou K, Shen H. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS ONE 2010, 5(4):9931.
33. Shen H, Chou K. A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal Biochem 2009, 394(2):269-274. 10.1016/j.ab.2009.07.046, 19651102.
34. Chou K, Shen H. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One 2010, 5(6):11335.
35. Shen H, Chou K. Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J Biomol Struct Dyn 2010, 28(2):175-186. 10.1080/07391102.2010.10507351, 20645651.
36. Lin H, Chen C, Sung T, Ho S, Hsu W. Protein subcellular localization prediction of eukaryotes using a knowledge-based approach. BMC Bioinformatics 2009, 10(Suppl 15):8. 10.1186/1471-2105-10-S15-S8, 2637845, 19128505.
37. He J, Gu H, Liu W. Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites. PLoS ONE 2012, 7(6):37155.
38. Grossman D, Domingos P. Learning Bayesian network classifiers by maximizing conditional likelihood. International Conference in Machine Learning, 2004. Proceedings 2004, 361-368. New York, NY, USA: ACM Press.
39. Höglund A, Dönnes P, Blum T, Adolph H, Kohlbacher O. MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs, and amino acid composition. Bioinformatics 2006, 22(10):1158-1165. 10.1093/bioinformatics/btl002, 16428265.
40. Garg A, Raghava G. ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins. BMC Bioinformatics 2008, 9:503. 10.1186/1471-2105-9-503, 2612013, 19038062.
41. Huang W, Tung C, Ho S, Hwang S, Ho S. Proloc-go: utilizing informative gene ontology terms for sequence-based prediction of protein subcellular localization. BMC Bioinformatics 2008, 9:80. 10.1186/1471-2105-9-80, 2262056, 18241343.
42. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. J Comput Biol 2000, 7(3-4):601-620. 10.1089/106652700750050961, 11108481.
43. Segal E, Taskar B, Gasch A, Friedman N, Koller D. Rich probabilistic models for gene expression. Bioinformatics 2001, 17(Suppl 1):243-252.
44. Lee P, Shatkay H. BNTagger: improved tagging SNP selection using Bayesian networks. Bioinformatics 2006, 22(14):211-219.
45. Jensen F, Nielsen T. Bayesian Networks and Decision Graphs 2007, London, UK: Springer.
46. Fayyad U, Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. International Joint Conferences on Artificial Intelligence, 1993. Proceedings 1993, 1022-1029. Burlington, MA, USA: Morgan Kaufmann.
47. Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretization of continuous features. International Conference in Machine Learning, 1995. Proceedings 1995, 194-202. Burlington, MA, USA: Morgan Kaufmann.
48. Heckerman D, Geiger D, Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 1995, 20(3):197-243.
49. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, VanderPlas F, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: machine learning in python. J Mach Learn Res 2011, 12:2825-2830.
50. Manning C, Raghavan P, Schütze H. Introduction to Information Retrieval 2008, New York, USA: Cambridge University Press.
51. Briesemeister S, Rahnenfuhrer J, Kohlbacher O. YLoc - An interpretable web server for predicting subcellular localization. Nucleic Acids Res 2010, 38(Web Server issue):497-502.
52. Chou K. Prediction of protein cellular attributes using pseudo-amino acid composition. Cell Mol Life Sci 2011, 43(3):246-255.
53. Cover T, Thomas J. Elements of Information Theory 2006, New Jersey, USA: Wiley.
54. Tung T, Lee D. A method to improve protein subcellular localization prediction by integrating various biological data sources. BMC Bioinformatics 2009, 10(Suppl 1):43. 10.1186/1471-2105-10-S1-S43, 2645366, 19187558.
55. Tsoumakas G, Katakis I, Vlahavas I. Mining multi-label data. Data Mining and Knowledge Discovery Handbook, 2010 2010, 667-685. New York, NY, USA: Springer.
56. DeGroot M, Schervish M. Probability and Statistics 2012, New Jersey, USA: Pearson Education.