Abstract
Sub-cellular localization prediction is an important step for inferring protein functions. Several strategies have been developed in the recent years to solve this problem, from alignment-based solutions to feature-based solutions. However, under some identity thesholds, these kind of approaches fail to detect homologous sequences, achieving predictions with low specificity and sensitivity. Here, a novel methodology is proposed for classifying proteins with low identity levels. This approach implements a simple, yet powerful assumption that employs hierarchical clustering and hidden Markov models, obtaining high performance on the prediction of four different sub-cellular localizations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chou, K.-C., Shen, H.-B.: Cell-ploc: a package of web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 3(2), 153–162 (2008)
Baldi, P., Brunak, S.: Bioinformatics: the machine learning approach. The MIT Press (2001)
Jaramillo-Garzón, J., Perera-Lluna, A., Castellanos-Domiínguez, C.: Predictability of protein subcellular locations by pattern recognition techniques. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5512–5515. IEEE (2010)
Conesa, A., Götz, S.: Blast2go: A comprehensive suite for functional analysis in plant genomics. International Journal of Plant Genomics 2008 (2008)
Hawkins, T., Chitale, M., Luban, S., Kihara, D.: PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data. Proteins 74(3), 566–582 (2009)
Yu, C., Lin, C., Hwang, J.: Predicting subcellular localization of proteins for gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Science 13(5), 1402–1406 (2004)
Shi, J., Zhang, S., Pan, Q., Cheng, Y., Xie, J.: Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids 33(1), 69–74 (2007)
Nanni, L., Lumini, A.: An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence. Amino Acids 35(3), 573–580 (2008)
Ma, J., Liu, W., Gu, H.: Predicting protein subcellular locations for Gram-negative bacteria using neural networks ensemble. In: Proceedings of the 6th Annual IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 114–120. The Institute of Electrical and Electronics Engineers Inc. (2009)
Shen, Y., Burger, G.: ‘Unite and conquer’: enhanced prediction of protein subcellular localization by integrating multiple specialized tools. BMC Bioinformatics 8(1), 420 (2007)
Shen, H., Yang, J., Chou, K.: Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 33(1), 57–67 (2007)
Niu, B., Jin, Y., Feng, K., Lu, W., Cai, Y., Li, G.: Using adaboost for the prediction of subcellular location of prokaryotic and eukaryotic proteins. Molecular Diversity 12(1), 41–45 (2008)
Khan, A., Majid, A., Choi, T.: Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers. Amino Acids 38(1), 347–350 (2010)
Punta, M., Coggill, P.C., Eberhardt, R.Y., Mistry, J., Tate, J., Boursnell, C., Pang, N., Forslund, K., Ceric, G., Clements, J., et al.: The pfam protein families database. Nucleic Acids Research 40(D1), D290–D301 (2012)
Arango-Argoty, G., Ruiz-Munoz, J., Jaramillo-Garzon, J., Castellanos-Dominguez, C.: An adaptation of pfam profiles to predict protein sub-cellular localization in gram positive bacteria. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5554–5557. IEEE (2012)
Chou, K.-C., Shen, H.-B.: Plant-mploc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PloS One 5(6), e11335 (2010)
Fu, L., Niu, B., Zhu, Z., Wu, S., Li, W.: Cd-hit: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23), 3150–3152 (2012)
Finn, R.D., Clements, J., Eddy, S.R.: Hmmer web server: interactive sequence similarity searching. Nucleic Acids Research 39(suppl. 2), W29–W37 (2011)
Jaramillo-Garzón, J.A., Gallardo-Chacón, J.J., Castellanos-Domínguez, C.G., Perera-Lluna, A.: Predictability of gene ontology slim-terms from primary structure information in embryophyta plant proteins. BMC Bioinformatics 14(1), 68 (2013)
Yooseph, S., Li, W., Sutton, G.: Gene identification and protein classification in microbial metagenomic sequence data via incremental clustering. BMC Bioinformatics 9(1), 182 (2008)
Sun, S., Chen, J., Li, W., Altintas, I., Lin, A., Peltier, S., Stocks, K., Allen, E.E., Ellisman, M., Grethe, J., et al.: Community cyberinfrastructure for advanced microbial ecology research and analysis: the camera resource. Nucleic Acids Research 39(suppl. 1), D546–D551 (2011)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Freyhult, E.K., Bollback, J.P., Gardner, P.P.: Exploring genomic dark matter: a critical assessment of the performance of homology search methods on noncoding rna. Genome Research 17(1), 117–125 (2007)
Sievers, F., Wilm, A., Dineen, D., Gibson, T.J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., et al.: Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Molecular Systems Biology 7(1) (2011)
Jain, E., Bairoch, A., Duvaud, S., Phan, I., Redaschi, N., Suzek, B., Martin, M., McGarvey, P., Gasteiger, E.: Infrastructure for the life sciences: design and implementation of the UniProt website. BMC Bioinformatics 10(1), 136 (2009)
Barrell, D., Dimmer, E., Huntley, R.P., Binns, D., O’Donovan, C., Apweiler, R.: The goa database in 2009-an integrated gene ontology annotation resource. Nucleic Acids Research 37(suppl. 1), D396–D403 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jaramillo-Garzón, J.A., Castro-Ceballos, J., Castellanos-Dominguez, G. (2015). Predicting Sub-cellular Location of Proteins Based on Hierarchical Clustering and Hidden Markov Models. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9044. Springer, Cham. https://doi.org/10.1007/978-3-319-16480-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-16480-9_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16479-3
Online ISBN: 978-3-319-16480-9
eBook Packages: Computer ScienceComputer Science (R0)