MemLoci: predicting subcellular localization of membrane proteins in eukaryotes
Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary...
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Veröffentlicht in: | Bioinformatics 2011-05, Vol.27 (9), p.1224-1230 |
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creator | PIERLEONI, Andrea MARTELLI, Pier Luigi CASADIO, Rita |
description | Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins.
Here we develop MemLoci, a new support vector machine-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50.
The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Datasets described in the article can be downloaded at the same site. |
doi_str_mv | 10.1093/bioinformatics/btr108 |
format | Article |
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Here we develop MemLoci, a new support vector machine-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50.
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Here we develop MemLoci, a new support vector machine-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50.
The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Datasets described in the article can be downloaded at the same site.</description><subject>Bioinformatics</subject><subject>Biological and medical sciences</subject><subject>Computational Biology - methods</subject><subject>Databases, Protein</subject><subject>Eukaryotic Cells - chemistry</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</subject><subject>Membrane Proteins - chemistry</subject><subject>Organelles - chemistry</subject><subject>Protein Sorting Signals</subject><subject>Protein Transport</subject><subject>Support Vector Machine</subject><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkD1PwzAQhi0EolD4CaAsiCn0HMeOw4YqvqSiLjBHjnNGhiQudjLAr8dVSxETk8_yc3evH0LOKFxRKNmsts72xvlODVaHWT14CnKPHFEmijSXlO7vamATchzCGwBw4OKQTLL1ixTlEVk-Ybdw2l4nK4-N1YPtX5Mw1hrbdmyVT1qnVWu_4hbXJ84kHXa1Vz1G3g1o-5DYPsHxXfnPeA8n5MCoNuDp9pySl7vb5_lDuljeP85vFqnOJRtSRo2UymgpC8iFgJhRCS2pZkwjQy0oyxqeATYImUFd8wLKUjOKRak5l2xKLjdzY4yPEcNQdTasQ8dobgxVSUGwIgf6LylFlglRFHkk-YbU3oXg0VQrb7v4sYpCtZZe_ZVebaTHvvPthrHusNl1_ViOwMUWUCHaNNGftuGXy6HkjJbsG7fdkK4</recordid><startdate>20110501</startdate><enddate>20110501</enddate><creator>PIERLEONI, Andrea</creator><creator>MARTELLI, Pier Luigi</creator><creator>CASADIO, Rita</creator><general>Oxford University Press</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20110501</creationdate><title>MemLoci: predicting subcellular localization of membrane proteins in eukaryotes</title><author>PIERLEONI, Andrea ; MARTELLI, Pier Luigi ; CASADIO, Rita</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c483t-31f88afc88704660803a6c81c33ce3ec6132d520ede02fecb57099c31e79c5583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Bioinformatics</topic><topic>Biological and medical sciences</topic><topic>Computational Biology - methods</topic><topic>Databases, Protein</topic><topic>Eukaryotic Cells - chemistry</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</topic><topic>Membrane Proteins - chemistry</topic><topic>Organelles - chemistry</topic><topic>Protein Sorting Signals</topic><topic>Protein Transport</topic><topic>Support Vector Machine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>PIERLEONI, Andrea</creatorcontrib><creatorcontrib>MARTELLI, Pier Luigi</creatorcontrib><creatorcontrib>CASADIO, Rita</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>PIERLEONI, Andrea</au><au>MARTELLI, Pier Luigi</au><au>CASADIO, Rita</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MemLoci: predicting subcellular localization of membrane proteins in eukaryotes</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2011-05-01</date><risdate>2011</risdate><volume>27</volume><issue>9</issue><spage>1224</spage><epage>1230</epage><pages>1224-1230</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><eissn>1460-2059</eissn><abstract>Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins.
Here we develop MemLoci, a new support vector machine-based tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50.
The MemLoci server is freely available on the web at: http://mu2py.biocomp.unibo.it/memloci. Datasets described in the article can be downloaded at the same site.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>21367869</pmid><doi>10.1093/bioinformatics/btr108</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Bioinformatics Biological and medical sciences Computational Biology - methods Databases, Protein Eukaryotic Cells - chemistry Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Membrane Proteins - chemistry Organelles - chemistry Protein Sorting Signals Protein Transport Support Vector Machine |
title | MemLoci: predicting subcellular localization of membrane proteins in eukaryotes |
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